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2025 Vol. 43, No. 12
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2025, 43(12): 1-12.
doi: 10.13205/j.hjgc.202512001
Abstract:
In the Anthropocene， elemental stoichiometry imbalance in water cycles may become a critical constraint on the sustainable development of river ecosystems. Regional development disparities have intensified the complexity of cross-border circulation of carbon， nitrogen， and phosphorus， posing challenges for pollution control due to spatial heterogeneity. Current effluent standards neglect the metabolic equilibrium thresholds of aquatic ecosystems， leading to mismatches between pollution reduction efficacy and environmental risk mitigation， as well as inadequate functional coordination of ecological metabolic systems post-treatment. In this regard， this study analyzed the spatiotemporal variations in carbon， nitrogen and phosphorus ratios across Chinese watersheds using multi-source data. Results revealed significant stoichiometric deviations during wastewater treatments from an influent C∶N∶P ratio of ［（354.07±121.33）∶（10.84±1.85）∶1］， to an effluent ratio of ［（73.44±26.52）∶（10.10±2.53）∶1］， indicating amplified nutrient disproportions and a disconnect between treatment strategies and the ecological functions of natural water bodies. Latitudinal gradients in element ratios highlighted human induced deviations from natural baselines ［ρ（C）∶ρ（N）∶ρ（P） = （136.30±51.24）∶（75.15±48.15）∶1］， with phosphorus limitation prevalent in outflow regions and water quality characterized by carbon constraints coupled with nutrient surpluses， which hindered plants utilization and biological mineralization. Therefore， water quality management in river basins was subject to common constraints of total reduction， elemental ratios reduction， and runoff mixing reduction. It was necessary to break through the scale limitations imposed by absolute concentration indicators in traditional water quality assessments from a stoichiometric perspective， emphasizing the elucidation of mechanisms， through which technological optimization and industrial upgrading influence elemental flows. This provided a scientific foundation for constructing a river basin governance system oriented by metabolic balance. Future research should further integrate hydrodynamic processes with ecological metabolic thresholds， delineating the spatial boundaries of eutrophication sensitive areas and their evolution trends influenced by climate and human activity changes， and achieving spatiotemporal coordination among technological emission reductions， residual pollutants and ecological feedback via models.
In the Anthropocene， elemental stoichiometry imbalance in water cycles may become a critical constraint on the sustainable development of river ecosystems. Regional development disparities have intensified the complexity of cross-border circulation of carbon， nitrogen， and phosphorus， posing challenges for pollution control due to spatial heterogeneity. Current effluent standards neglect the metabolic equilibrium thresholds of aquatic ecosystems， leading to mismatches between pollution reduction efficacy and environmental risk mitigation， as well as inadequate functional coordination of ecological metabolic systems post-treatment. In this regard， this study analyzed the spatiotemporal variations in carbon， nitrogen and phosphorus ratios across Chinese watersheds using multi-source data. Results revealed significant stoichiometric deviations during wastewater treatments from an influent C∶N∶P ratio of ［（354.07±121.33）∶（10.84±1.85）∶1］， to an effluent ratio of ［（73.44±26.52）∶（10.10±2.53）∶1］， indicating amplified nutrient disproportions and a disconnect between treatment strategies and the ecological functions of natural water bodies. Latitudinal gradients in element ratios highlighted human induced deviations from natural baselines ［ρ（C）∶ρ（N）∶ρ（P） = （136.30±51.24）∶（75.15±48.15）∶1］， with phosphorus limitation prevalent in outflow regions and water quality characterized by carbon constraints coupled with nutrient surpluses， which hindered plants utilization and biological mineralization. Therefore， water quality management in river basins was subject to common constraints of total reduction， elemental ratios reduction， and runoff mixing reduction. It was necessary to break through the scale limitations imposed by absolute concentration indicators in traditional water quality assessments from a stoichiometric perspective， emphasizing the elucidation of mechanisms， through which technological optimization and industrial upgrading influence elemental flows. This provided a scientific foundation for constructing a river basin governance system oriented by metabolic balance. Future research should further integrate hydrodynamic processes with ecological metabolic thresholds， delineating the spatial boundaries of eutrophication sensitive areas and their evolution trends influenced by climate and human activity changes， and achieving spatiotemporal coordination among technological emission reductions， residual pollutants and ecological feedback via models.
2025, 43(12): 13-20.
doi: 10.13205/j.hjgc.202512002
Abstract:
This study presented an analysis of the emission characteristics of 13 phenolic compounds within coal chemical industry parks situated in the middle reach of the Yellow River Basin. The contribution and impact of industrial wastewater on the prevalence of phenolic pollutants in the mainstream of the Yellow River were investigated. A total of 6 and 7 phenolic pollutants were identified in 5 industrial wastewater discharge outlets and 18 river cross-section sampling sites （including the tributary sampling sites such as the Fen River）， with the total concentration range from 115.66 to 38462.44 ng/L， and 44.13 to 2568.32 ng/L， respectively. Phenol， o-cresol， and p-cresol were the most dominant pollutants at these sampling sites. The total concentration of phenols discharged at the industrial wastewater discharge outlets was generally higher than that at the upstream and downstream sections. Sampling sites within the same industrial area exhibited similar types and concentration proportions of pollutants， suggesting that emissions from industrial areas were one of the main sources of phenolic pollution in the surface water of the Yellow River Basin. The ecological risk quotient method was employed to assess the risk of these phenolic pollutions， and the results indicated that phenol， p-cresol， 2-chlorophenol， and 2-nitrophenol posed potential ecological risks in the majority of sampling sites， while ecological risks were prevalent in the sampling points of the middle and lower reaches of the Fen River. Therefore， these phenolic pollutants and the middle and lower reaches of the Fen River basin require focused attention. This study can provide theoretical support and a scientific basis for phenol pollution control in the middle reach of the Yellow River Basin.
This study presented an analysis of the emission characteristics of 13 phenolic compounds within coal chemical industry parks situated in the middle reach of the Yellow River Basin. The contribution and impact of industrial wastewater on the prevalence of phenolic pollutants in the mainstream of the Yellow River were investigated. A total of 6 and 7 phenolic pollutants were identified in 5 industrial wastewater discharge outlets and 18 river cross-section sampling sites （including the tributary sampling sites such as the Fen River）， with the total concentration range from 115.66 to 38462.44 ng/L， and 44.13 to 2568.32 ng/L， respectively. Phenol， o-cresol， and p-cresol were the most dominant pollutants at these sampling sites. The total concentration of phenols discharged at the industrial wastewater discharge outlets was generally higher than that at the upstream and downstream sections. Sampling sites within the same industrial area exhibited similar types and concentration proportions of pollutants， suggesting that emissions from industrial areas were one of the main sources of phenolic pollution in the surface water of the Yellow River Basin. The ecological risk quotient method was employed to assess the risk of these phenolic pollutions， and the results indicated that phenol， p-cresol， 2-chlorophenol， and 2-nitrophenol posed potential ecological risks in the majority of sampling sites， while ecological risks were prevalent in the sampling points of the middle and lower reaches of the Fen River. Therefore， these phenolic pollutants and the middle and lower reaches of the Fen River basin require focused attention. This study can provide theoretical support and a scientific basis for phenol pollution control in the middle reach of the Yellow River Basin.
2025, 43(12): 21-27.
doi: 10.13205/j.hjgc.202512003
Abstract:
To address the water environment issues in manganese mining areas， this study took a typical manganese mining area as the research object. By collecting four types of water samples including spring water， leachate water， stream water， and mine gushing water， and applying hydrochemical analysis， the single-factor pollution index method， and the mean-type comprehensive pollution index method， combined with spatial statistics and correlation analysis， the hydrochemical characteristics， pollution status， and spatial differentiation patterns of the mining area’s water environment were revealed. The research findings were as follows： 1） mine gushing water and stream water exhibited “three high” characteristics： high total dissolved solids （TDS）， high sulfate （SO2-4）， and high manganese （Mn 2+ ）， while spring water was less affected by mining activities； 2） water pollution in the mining area was generally severe， with 86.96% of mine gushing water samples and 66.10% of stream water samples classified as extremely heavily polluted. SO2-4 was identified as the primary pollutant， and Mn 2+ was the main heavy metal pollutant； 3） pollution showed significant spatial differentiation. The Shuitianxi River basin was dominated by point sources from mine gushing water， while the Mao’er River and Longmenxi River basins were driven by leaching from waste rock piles and groundwater connectivity， forming a ternary pollution model of “mine waste-runoff-groundwater connectivity”.
To address the water environment issues in manganese mining areas， this study took a typical manganese mining area as the research object. By collecting four types of water samples including spring water， leachate water， stream water， and mine gushing water， and applying hydrochemical analysis， the single-factor pollution index method， and the mean-type comprehensive pollution index method， combined with spatial statistics and correlation analysis， the hydrochemical characteristics， pollution status， and spatial differentiation patterns of the mining area’s water environment were revealed. The research findings were as follows： 1） mine gushing water and stream water exhibited “three high” characteristics： high total dissolved solids （TDS）， high sulfate （SO2-4）， and high manganese （Mn 2+ ）， while spring water was less affected by mining activities； 2） water pollution in the mining area was generally severe， with 86.96% of mine gushing water samples and 66.10% of stream water samples classified as extremely heavily polluted. SO2-4 was identified as the primary pollutant， and Mn 2+ was the main heavy metal pollutant； 3） pollution showed significant spatial differentiation. The Shuitianxi River basin was dominated by point sources from mine gushing water， while the Mao’er River and Longmenxi River basins were driven by leaching from waste rock piles and groundwater connectivity， forming a ternary pollution model of “mine waste-runoff-groundwater connectivity”.
2025, 43(12): 28-37.
doi: 10.13205/j.hjgc.202512004
Abstract:
By concentrating low-strength domestic wastewater， a sewage concentrate with high organic matter concentration can be obtained， and chemical energy can be recovered through anaerobic digestion （AD）. In this study， based on the fraction analysis of real domestic wastewater， a direct membrane filtration （DMF） process and an electroflocculation-membrane （EFM） process were constructed to concentrate domestic wastewater， and after 1 concentration cycle， the DMF process obtained a concentrate with chemical oxygen demand （COD） concentration of 1760 mg/L， and the EFM process obtained a concentrate with COD concentration of 2668.5 mg/L. Both DMF and EFM process were able to effectively retain organic matter， and the percentage of particles with particle size larger than 40 μm was increased by about 30.1% and 21.8%， respectively， compared with domestic wastewater. The EFM process also facilitated the conversion of particles in the dissolved state and the colloidal state to the suspended state， and significantly improved the concentration effect of total nitrogen （TN） and total phosphorus （TP）， with the concentration efficiency reaching 23.9% and 22.6%， respectively. Due to the iron anodes in the electroflocculation process， the EFM concentrate also introduced iron species such as Fe2O3， Fe3O4， and Fe2.95（PO4）2（OH）2， and Fe（II）/Fe（Ⅲ） were mainly present in the amorphous form. The results of the biochemical methane potential （BMP） test showed that the DMF concentrate possessed a higher methanogenic potential （268.58 to 281.76 mL CH4/g COD）， while the EFM concentrate showed stronger methanogenic activity only in the fractions with particle sizes less than 5 μm and 0.45 μm， with methanogenic potentials （P0） of （328.71±10.85） and （317.84±10.20） mL CH4/g COD， respectively. The energy consumption of the EFM system for treating 1 m3 of domestic wastewater was 298.20×10-4 kW·h/m3， higher than that of DMF （20.04×10-4 kW·h/m3）， but its net energy production capacity after coupling with AD amounted to 0.208 kW·h/m3， which was about 1.5 times higher than that of DMF coupling with AD （0.081 kW·h/m3）， and indicating that the EFM system had a significant wastewater energization advantage. This study provides a useful technical reference and theoretical basis for wastewater treatment and resource recovery.
By concentrating low-strength domestic wastewater， a sewage concentrate with high organic matter concentration can be obtained， and chemical energy can be recovered through anaerobic digestion （AD）. In this study， based on the fraction analysis of real domestic wastewater， a direct membrane filtration （DMF） process and an electroflocculation-membrane （EFM） process were constructed to concentrate domestic wastewater， and after 1 concentration cycle， the DMF process obtained a concentrate with chemical oxygen demand （COD） concentration of 1760 mg/L， and the EFM process obtained a concentrate with COD concentration of 2668.5 mg/L. Both DMF and EFM process were able to effectively retain organic matter， and the percentage of particles with particle size larger than 40 μm was increased by about 30.1% and 21.8%， respectively， compared with domestic wastewater. The EFM process also facilitated the conversion of particles in the dissolved state and the colloidal state to the suspended state， and significantly improved the concentration effect of total nitrogen （TN） and total phosphorus （TP）， with the concentration efficiency reaching 23.9% and 22.6%， respectively. Due to the iron anodes in the electroflocculation process， the EFM concentrate also introduced iron species such as Fe2O3， Fe3O4， and Fe2.95（PO4）2（OH）2， and Fe（II）/Fe（Ⅲ） were mainly present in the amorphous form. The results of the biochemical methane potential （BMP） test showed that the DMF concentrate possessed a higher methanogenic potential （268.58 to 281.76 mL CH4/g COD）， while the EFM concentrate showed stronger methanogenic activity only in the fractions with particle sizes less than 5 μm and 0.45 μm， with methanogenic potentials （P0） of （328.71±10.85） and （317.84±10.20） mL CH4/g COD， respectively. The energy consumption of the EFM system for treating 1 m3 of domestic wastewater was 298.20×10-4 kW·h/m3， higher than that of DMF （20.04×10-4 kW·h/m3）， but its net energy production capacity after coupling with AD amounted to 0.208 kW·h/m3， which was about 1.5 times higher than that of DMF coupling with AD （0.081 kW·h/m3）， and indicating that the EFM system had a significant wastewater energization advantage. This study provides a useful technical reference and theoretical basis for wastewater treatment and resource recovery.
2025, 43(12): 38-47.
doi: 10.13205/j.hjgc.202512005
Abstract:
Partial nitrification （PN） is an important avenue to achieve low-carbon and efficient nitrogen removal under the Dual-Carbon goals， and increasingly stringent wastewater discharge requirements. This paper reviews and summarizes studies on achieving PN through the inhibition of free nitrous acid （FNA）. The antibacterial mechanisms of FNA， which primarily include reducing intracellular pH and decomposing into active species with strong antibacterial effects， are analysed. The differential responses of ammonia-oxidizing bacteria （AOB） and nitrite-oxidizing bacteria （NOB） are summarized， with emphasis on the enrichment of FNA-tolerant NOB （Candidatus Nitrotoga）， as a key factor causing a failure in FNA-mediated PN. Challenges in achieving long-term stable operation of PN under FNA inhibition are analysed， and combining FNA inhibition with acid-tolerant AOB to construct acidic PN， or integrating FNA with other inhibitory factors with different mechanisms are identified as effective approaches to counteract NOB adaptability. FNA inhibition strategies， including mainstream in-situ inhibition and sidestream sludge pretreatment， are comprehensively analysed， highlighting their technical characteristics and applicable scopes. Finally， the future development of FNA-driven PN regulation strategies is discussed. This paper can provide a valuable reference for the research and application of low-carbon and efficient nitrogen removal technologies regulated by FNA.
Partial nitrification （PN） is an important avenue to achieve low-carbon and efficient nitrogen removal under the Dual-Carbon goals， and increasingly stringent wastewater discharge requirements. This paper reviews and summarizes studies on achieving PN through the inhibition of free nitrous acid （FNA）. The antibacterial mechanisms of FNA， which primarily include reducing intracellular pH and decomposing into active species with strong antibacterial effects， are analysed. The differential responses of ammonia-oxidizing bacteria （AOB） and nitrite-oxidizing bacteria （NOB） are summarized， with emphasis on the enrichment of FNA-tolerant NOB （Candidatus Nitrotoga）， as a key factor causing a failure in FNA-mediated PN. Challenges in achieving long-term stable operation of PN under FNA inhibition are analysed， and combining FNA inhibition with acid-tolerant AOB to construct acidic PN， or integrating FNA with other inhibitory factors with different mechanisms are identified as effective approaches to counteract NOB adaptability. FNA inhibition strategies， including mainstream in-situ inhibition and sidestream sludge pretreatment， are comprehensively analysed， highlighting their technical characteristics and applicable scopes. Finally， the future development of FNA-driven PN regulation strategies is discussed. This paper can provide a valuable reference for the research and application of low-carbon and efficient nitrogen removal technologies regulated by FNA.
2025, 43(12): 48-55.
doi: 10.13205/j.hjgc.202512006
Abstract:
In response to the common problem of high nitrogen in raw water and low carbon to nitrogen ratio， which leads to unstable effluent in village and town sewage， especially the poor removal effect of total nitrogen， a 2.0 m3/d inverted AAO type MABR treatment process pilot plant was built in a town-level sewage treatment plant in Chongqing to investigate its actual effect in treating high influent total nitrogen sewage. This study investigated the biofilm formation efficiency during the reactor startup period， as well as the effects of key operational parameters during the operational phase， such as internal reflux ratio， sludge concentration， and influent carbon-to-nitrogen ratio （C/N） on pollutant removal efficiency. The experimental results showed that the water temperature ranged from 13℃ to 20℃， COD concentration in the influent water was 255 to 324 mg/L， the TN influent concentration was 39.1 to 55.6 mg/L， the suspended sludge concentration was about 3000 mg/L， and the successful hanging time of the aeration membrane was about 40 days. The operating conditions without internal reflux had great adverse effect on the removal of COD， ammonia nitrogen， and total nitrogen. At this time， the removal rate of total nitrogen was about 40%. After the reflux ratio reached 200%， stable removal efficiencies for COD and NH3-N were achieved， and further increase in reflux ratio showed no significant improvement on their removal. However， the removal rate of total nitrogen increased with the increase in reflux ratio. When the reflux ratio reached 500%， the maximum total nitrogen removal rate reached 90%. When the sludge concentration reached 2000 mg/L， the fluctuation of intake water quality had little impact and could meet the emission standard stably. When the influent C/N ratio was 2.8 and 3.6， respectively， the influent total nitrogen concentrations ranged from 40 to 100.1 mg/L， the average effluent total nitrogen was 13.8 mg/L， and the average total nitrogen removal rate was 83.1%. When the C/N ratio reached 4.2 or higher， the total nitrogen concentration was 42 to 58 mg/L， the effluent total nitrogen decreased to 6.3 to 8.3 mg/L， the average concentration was 7.3 mg/L， and the average removal rate was 83.9%. The inverted AAO type MABR process exhibited good stability and enhanced denitrification performance under high nitrogen inflow and low carbon-to-nitrogen （C/N） ratio conditions caused by high nitrogen. Membrane aeration is suitable for improving the quality and efficiency of village and town sewage due to convenient management without backwashing.
In response to the common problem of high nitrogen in raw water and low carbon to nitrogen ratio， which leads to unstable effluent in village and town sewage， especially the poor removal effect of total nitrogen， a 2.0 m3/d inverted AAO type MABR treatment process pilot plant was built in a town-level sewage treatment plant in Chongqing to investigate its actual effect in treating high influent total nitrogen sewage. This study investigated the biofilm formation efficiency during the reactor startup period， as well as the effects of key operational parameters during the operational phase， such as internal reflux ratio， sludge concentration， and influent carbon-to-nitrogen ratio （C/N） on pollutant removal efficiency. The experimental results showed that the water temperature ranged from 13℃ to 20℃， COD concentration in the influent water was 255 to 324 mg/L， the TN influent concentration was 39.1 to 55.6 mg/L， the suspended sludge concentration was about 3000 mg/L， and the successful hanging time of the aeration membrane was about 40 days. The operating conditions without internal reflux had great adverse effect on the removal of COD， ammonia nitrogen， and total nitrogen. At this time， the removal rate of total nitrogen was about 40%. After the reflux ratio reached 200%， stable removal efficiencies for COD and NH3-N were achieved， and further increase in reflux ratio showed no significant improvement on their removal. However， the removal rate of total nitrogen increased with the increase in reflux ratio. When the reflux ratio reached 500%， the maximum total nitrogen removal rate reached 90%. When the sludge concentration reached 2000 mg/L， the fluctuation of intake water quality had little impact and could meet the emission standard stably. When the influent C/N ratio was 2.8 and 3.6， respectively， the influent total nitrogen concentrations ranged from 40 to 100.1 mg/L， the average effluent total nitrogen was 13.8 mg/L， and the average total nitrogen removal rate was 83.1%. When the C/N ratio reached 4.2 or higher， the total nitrogen concentration was 42 to 58 mg/L， the effluent total nitrogen decreased to 6.3 to 8.3 mg/L， the average concentration was 7.3 mg/L， and the average removal rate was 83.9%. The inverted AAO type MABR process exhibited good stability and enhanced denitrification performance under high nitrogen inflow and low carbon-to-nitrogen （C/N） ratio conditions caused by high nitrogen. Membrane aeration is suitable for improving the quality and efficiency of village and town sewage due to convenient management without backwashing.
2025, 43(12): 56-62.
doi: 10.13205/j.hjgc.202512007
Abstract:
Despite the presence of humic substances in the digestate of sewage treatment plants deteriorates the phosphorus （P） recovery efficiency of calcium phosphate （CaP） crystallization， it dose improve the phytoavailability of CaP when used as P fertilizer. To overcome the inhibitory effect of humic substances on CaP crystallization， phosphorus and humic substances were co-recovered efficiently using a fluidized-bed reactor through optimization of Ca/P molar ratio and stirring intensity. The results showed that recovery efficiencies of 73% for P， and 53% for fulvic acid （FA）， were achieved at an initial P concentration of 30 mg/L， an FA concentration of 25 mg/L， a stirring speed of 100 r/min， and a Ca/P molar ratio of 5. FA was proved to delay the crystallization process of CaP via hydrogen bonding and carboxyl surface complexation. Stirring promoted the nucleation of CaP and facilitated the aggregation of nuclei， thereby enhancing P recovery efficiency. Meanwhile， high FA recovery efficiency was obtained by the co-precipitation of FA and CaP and FA adsorption on the CaP surface. FA recovery efficiency can be further enhanced through Ca²⁺ bridging between FA in the liquid phase and FA adsorbed on the CaP surface. Within the tested pH range of 7 to 10， P recovery efficiency increased with the increasing of pH value， while FA recovery efficiency initially increased and then decreased， peaking at 53% at pH 9. In this case， CaP crystallization products contain hydroxyapatite （HAP） and its precursors.
Despite the presence of humic substances in the digestate of sewage treatment plants deteriorates the phosphorus （P） recovery efficiency of calcium phosphate （CaP） crystallization， it dose improve the phytoavailability of CaP when used as P fertilizer. To overcome the inhibitory effect of humic substances on CaP crystallization， phosphorus and humic substances were co-recovered efficiently using a fluidized-bed reactor through optimization of Ca/P molar ratio and stirring intensity. The results showed that recovery efficiencies of 73% for P， and 53% for fulvic acid （FA）， were achieved at an initial P concentration of 30 mg/L， an FA concentration of 25 mg/L， a stirring speed of 100 r/min， and a Ca/P molar ratio of 5. FA was proved to delay the crystallization process of CaP via hydrogen bonding and carboxyl surface complexation. Stirring promoted the nucleation of CaP and facilitated the aggregation of nuclei， thereby enhancing P recovery efficiency. Meanwhile， high FA recovery efficiency was obtained by the co-precipitation of FA and CaP and FA adsorption on the CaP surface. FA recovery efficiency can be further enhanced through Ca²⁺ bridging between FA in the liquid phase and FA adsorbed on the CaP surface. Within the tested pH range of 7 to 10， P recovery efficiency increased with the increasing of pH value， while FA recovery efficiency initially increased and then decreased， peaking at 53% at pH 9. In this case， CaP crystallization products contain hydroxyapatite （HAP） and its precursors.
2025, 43(12): 63-72.
doi: 10.13205/j.hjgc.202512008
Abstract:
At present， China's aquatic ecosystem is facing severe challenges， and the task of water environment management is urgent. Implementing multi-objective optimization scheduling strategies for efficient management of water environment management project groups is of significant importance for advancing the process of water environment management. By comprehensively considering resource constraints and the precedence relationships among multiple sub-projects， a multi-objective optimization model for the construction period-robustness of the Maozhou River Water Environment Management Project Group was constructed. Combined with the multi-population NSGA-Ⅲ algorithm， a double-layer encoding approach was used for solving the problem. With specified parameter calculations， an optimized scheduling frontier solution set for the project group's construction period robustness was obtained. It was found that when the project's robustness was high， the construction period was relatively longer. Overall， the Pareto solution set showed a non-linear increasing distribution in the objective space， validating the rationality of the model and the effectiveness of the NSGA-Ⅲ algorithm in solving scheduling problems for water environment management project groups. This study provides a reference for other cities to carry out watershed water environment management and helps to improve the comprehensive management capabilities and decision-making levels of such water environment management project groups.
At present， China's aquatic ecosystem is facing severe challenges， and the task of water environment management is urgent. Implementing multi-objective optimization scheduling strategies for efficient management of water environment management project groups is of significant importance for advancing the process of water environment management. By comprehensively considering resource constraints and the precedence relationships among multiple sub-projects， a multi-objective optimization model for the construction period-robustness of the Maozhou River Water Environment Management Project Group was constructed. Combined with the multi-population NSGA-Ⅲ algorithm， a double-layer encoding approach was used for solving the problem. With specified parameter calculations， an optimized scheduling frontier solution set for the project group's construction period robustness was obtained. It was found that when the project's robustness was high， the construction period was relatively longer. Overall， the Pareto solution set showed a non-linear increasing distribution in the objective space， validating the rationality of the model and the effectiveness of the NSGA-Ⅲ algorithm in solving scheduling problems for water environment management project groups. This study provides a reference for other cities to carry out watershed water environment management and helps to improve the comprehensive management capabilities and decision-making levels of such water environment management project groups.
2025, 43(12): 73-80.
doi: 10.13205/j.hjgc.202512009
Abstract:
With the addition of hydrazine （N2H4）， Anammox’s low activity in municipal wastewater applications can be increased. To investigate the short-term effects of various N2H4 concentrations on the nitrogen removal capability of ANAMMOX biofilms， the change in matrix degradation rate， N2H4 degradation rate， the stoichiometric ratio of chemical reaction， extracellular polymer （EPS）， heme， and microbial community were analyzed. The results showed that the addition of N2H4 increased the consumption rate of NO-2-N and decreased the formation rate of NO-3-N. When the concentration of N2H4 was 20 mg/L， the highest total nitrogen removal efficiency （NRR）of 78.35 mg/（L·d） was achieved， which was three times higher than the control group. The analysis of EPS extracted from biomass showed that， the maximum concentration of TB-EPS was 10.55 mg/g when the concentration of N2H4 was 20 mg/L， that was 0.9 times higher than the control， and the increase in TB-EPS concetration can inprove the stability of the biofilm； the highest concentration of heme was 3.00 μmol/g when the concentration of N2H4 was 20 mg/L. As a result， short-term addition of 20 to 40 mg/L of N2H4 can promote the growth of AnAOB and enchance the stability of the ANAMMOX biofilm system.
With the addition of hydrazine （N2H4）， Anammox’s low activity in municipal wastewater applications can be increased. To investigate the short-term effects of various N2H4 concentrations on the nitrogen removal capability of ANAMMOX biofilms， the change in matrix degradation rate， N2H4 degradation rate， the stoichiometric ratio of chemical reaction， extracellular polymer （EPS）， heme， and microbial community were analyzed. The results showed that the addition of N2H4 increased the consumption rate of NO-2-N and decreased the formation rate of NO-3-N. When the concentration of N2H4 was 20 mg/L， the highest total nitrogen removal efficiency （NRR）of 78.35 mg/（L·d） was achieved， which was three times higher than the control group. The analysis of EPS extracted from biomass showed that， the maximum concentration of TB-EPS was 10.55 mg/g when the concentration of N2H4 was 20 mg/L， that was 0.9 times higher than the control， and the increase in TB-EPS concetration can inprove the stability of the biofilm； the highest concentration of heme was 3.00 μmol/g when the concentration of N2H4 was 20 mg/L. As a result， short-term addition of 20 to 40 mg/L of N2H4 can promote the growth of AnAOB and enchance the stability of the ANAMMOX biofilm system.
2025, 43(12): 81-91.
doi: 10.13205/j.hjgc.202512010
Abstract:
Water pollution source tracing is an important aspect of environmental monitoring. In recent years， pollution source tracing technologies based on numerical methods have gained widespread attention. However， existing numerical source tracing methods often consider only the information of a single pollutant， neglecting the simultaneous discharge of different types of pollutants. It is entirely possible to effectively utilize information from various pollutants to enhance the reliability and accuracy of numerical source tracing. To this end， this study used the AM-MCMC Bayesian inference source tracing model as the baseline numerical tracing algorithm and simulated a scenario of three pollutants being discharged simultaneously and at the same location. Two pollutant information fusion paths were designed， and the effect of combining data assimilation on the performance enhancement of numerical source tracing was investigated. The results showed that information fusion significantly improved the inversion performance of two source parameters， the emission time and location， which have relatively large errors. In particular， the information fusion path weighted by the Markov chain yielded significant improvements. For example， the uncertainty range （by 95% confidence interval） of the emission location inversion result was reduced from an average of 10% to 1%， and the accuracy （relative mean error） improved from 9.6%~18.6%， to 2.9%. The framework of information fusion combined with data assimilation didn't increase the monitoring burden. It only required additional existing water quality parameters to significantly enhance the robustness of the source tracing， demonstrating high value in practice.
Water pollution source tracing is an important aspect of environmental monitoring. In recent years， pollution source tracing technologies based on numerical methods have gained widespread attention. However， existing numerical source tracing methods often consider only the information of a single pollutant， neglecting the simultaneous discharge of different types of pollutants. It is entirely possible to effectively utilize information from various pollutants to enhance the reliability and accuracy of numerical source tracing. To this end， this study used the AM-MCMC Bayesian inference source tracing model as the baseline numerical tracing algorithm and simulated a scenario of three pollutants being discharged simultaneously and at the same location. Two pollutant information fusion paths were designed， and the effect of combining data assimilation on the performance enhancement of numerical source tracing was investigated. The results showed that information fusion significantly improved the inversion performance of two source parameters， the emission time and location， which have relatively large errors. In particular， the information fusion path weighted by the Markov chain yielded significant improvements. For example， the uncertainty range （by 95% confidence interval） of the emission location inversion result was reduced from an average of 10% to 1%， and the accuracy （relative mean error） improved from 9.6%~18.6%， to 2.9%. The framework of information fusion combined with data assimilation didn't increase the monitoring burden. It only required additional existing water quality parameters to significantly enhance the robustness of the source tracing， demonstrating high value in practice.
2025, 43(12): 92-101.
doi: 10.13205/j.hjgc.202512011
Abstract:
In response to the problems of unstable treatment effects on chemical oxygen demand （COD） and nitrogen-containing pollutants in water， and poor activity of functional bacterial communities in existing coking wastewater treatment processes， this paper explored the treatment efficiency and related pollutants’ degradation laws of a two-stage cascade sequencing batch reactor for coking wastewater treatment. The results showed that the removal rates of COD， ammonia nitrogen， and volatile phenols by the two-stage series sequencing batch reactor were relatively stable at 88.33%， 89.29%， and 92.86% after the first stage treatment， and at 95.83%， 96.43%， and 98.57% after the second stage treatment. The primary removal rates of COD， ammonia nitrogen， volatile phenols， and pyridine， quinoline， and indole in nitrogen heterocyclic organic compounds in coking wastewater were 48.3%， 87.13%， and 100%， respectively， and the secondary removal rates were 91.09%， 91.82%， and 100%， respectively； acute biological toxicity was reduced by more than 98%. Most pollutants were degraded in the aerobic stage of primary treatment， and residual pollutants can be further removed in subsequent stages. The microbial community that had been selectively domesticated through changes in pollutants’ gradient concentration showed a good tolerance to the main pollutants’ species in wastewater， and the abundance of related functional microbial communities was relatively high， mostly facultative bacteria. This research proued that a two-stage series sequencing batch reactor can achieve efficient and stable treatment of coking wastewater.
In response to the problems of unstable treatment effects on chemical oxygen demand （COD） and nitrogen-containing pollutants in water， and poor activity of functional bacterial communities in existing coking wastewater treatment processes， this paper explored the treatment efficiency and related pollutants’ degradation laws of a two-stage cascade sequencing batch reactor for coking wastewater treatment. The results showed that the removal rates of COD， ammonia nitrogen， and volatile phenols by the two-stage series sequencing batch reactor were relatively stable at 88.33%， 89.29%， and 92.86% after the first stage treatment， and at 95.83%， 96.43%， and 98.57% after the second stage treatment. The primary removal rates of COD， ammonia nitrogen， volatile phenols， and pyridine， quinoline， and indole in nitrogen heterocyclic organic compounds in coking wastewater were 48.3%， 87.13%， and 100%， respectively， and the secondary removal rates were 91.09%， 91.82%， and 100%， respectively； acute biological toxicity was reduced by more than 98%. Most pollutants were degraded in the aerobic stage of primary treatment， and residual pollutants can be further removed in subsequent stages. The microbial community that had been selectively domesticated through changes in pollutants’ gradient concentration showed a good tolerance to the main pollutants’ species in wastewater， and the abundance of related functional microbial communities was relatively high， mostly facultative bacteria. This research proued that a two-stage series sequencing batch reactor can achieve efficient and stable treatment of coking wastewater.
2025, 43(12): 102-111.
doi: 10.13205/j.hjgc.202512012
Abstract:
Aiming at the problems of large carbon demand and high energy consumption of the traditional wastewater treatment process， this study constructed a pilot plant of SPDA-MBR. The influent was prepared using real urban wastewater to investigate the denitrification effect of the SPDA-MBR process on urban wastewater and analyze its mechanism. The results indicated that， under the operational conditions of 17.8 to 27.9 ℃ for the partial denitrification reactor （PD tank）， and 33.2 to 36.3 ℃ for the anaerobic ammonium oxidation membrane bioreactor （Anammox-MBR）， the average TN removal rate of the SPDA-MBR process was 89.59%. The nitrogen removal loading of the PD pool and Anammox-MBR pool were 0.13 kg N/（m3·d） and 0.15 kg N/（m3·d）， respectively， contributing 53.94% and 46.06% to the total nitrogen removal. High-throughput sequencing results showed that two partial denitrifying bacteria， Thauera and Thermomonas， were successfully enriched in the PD pool， with relative abundances of 1.7% and 4.01%， respectively. The relative abundance of Candidatus_Jettenia， a typical AnAOB genus， decreased from 13.87% to 6.16%， due to the impact of disturbances before and after the operational period， but the relative abundance of anammox companion genera SM1A02 and SWB02 increased， indicating that the synergistic effect among the complex bacterial species is beneficial to maintaining a higher anammox effect in the urban wastewater treatment process. This further suggests that the key to strengthening the stable and efficient nitrogen removal of urban wastewater by the SPDA-MBR process， is to maintain the synergistic effect of anammox and denitrification and other nitrogen removal pathways.
Aiming at the problems of large carbon demand and high energy consumption of the traditional wastewater treatment process， this study constructed a pilot plant of SPDA-MBR. The influent was prepared using real urban wastewater to investigate the denitrification effect of the SPDA-MBR process on urban wastewater and analyze its mechanism. The results indicated that， under the operational conditions of 17.8 to 27.9 ℃ for the partial denitrification reactor （PD tank）， and 33.2 to 36.3 ℃ for the anaerobic ammonium oxidation membrane bioreactor （Anammox-MBR）， the average TN removal rate of the SPDA-MBR process was 89.59%. The nitrogen removal loading of the PD pool and Anammox-MBR pool were 0.13 kg N/（m3·d） and 0.15 kg N/（m3·d）， respectively， contributing 53.94% and 46.06% to the total nitrogen removal. High-throughput sequencing results showed that two partial denitrifying bacteria， Thauera and Thermomonas， were successfully enriched in the PD pool， with relative abundances of 1.7% and 4.01%， respectively. The relative abundance of Candidatus_Jettenia， a typical AnAOB genus， decreased from 13.87% to 6.16%， due to the impact of disturbances before and after the operational period， but the relative abundance of anammox companion genera SM1A02 and SWB02 increased， indicating that the synergistic effect among the complex bacterial species is beneficial to maintaining a higher anammox effect in the urban wastewater treatment process. This further suggests that the key to strengthening the stable and efficient nitrogen removal of urban wastewater by the SPDA-MBR process， is to maintain the synergistic effect of anammox and denitrification and other nitrogen removal pathways.
2025, 43(12): 112-120.
doi: 10.13205/j.hjgc.202512013
Abstract:
The efficacy of groundwater pollution risk control is often limited by a lack of information on spatial correlations between groundwater vulnerabilities and pollution sources. To overcome this limitation， a method was established to reveal the spatial correlations between groundwater vulnerabilities and industrial pollution sources in an industrialized city of Guangdong Province， China， using a combination of genetic algorithm （GA）， back propagation neural network （BPNN）， kernel density estimation （KDE）， and bivariate local Moran’s I （BLMI）. The subjectivity in the indicator weighting of a DRASTICL model was successfully reduced by GA-BPNN， and then the groundwater vulnerability map was created by the GA-BPNN-DRASTICL model. A spatial distribution map of the industrial pollution source distribution was produced by KDE. The spatial clustering map between groundwater vulnerabilities and industrial pollution sources was generated by BLMI， explicitly showing their distribution characteristics and implying that specific measures should be taken to control risks of groundwater pollution in different parts of the study area. The results showed that the best accuracy of the GA-BPNN algorithm was obtained， with the training function of trainlm， the neuron number of 6 in the hidden layer， the learning rate of 0.1， the population size of 40， the crossover rate of 0.6， and the mutation rate of 0.01. The best indicator weights for depth to the water table， net recharge， aquifer medium， soil medium， topography， impact of vadose zone， hydraulic conductivity， and land use were 2.84， 5.27， 0.84， 2.20， 2.36， 6.58， 1.21， and 6.70， respectively. The high and very high vulnerability classes were concentrated in the central and southern parts of the study area. The largest hotspot of the industrial pollution sources was located in the midwest part， and the high-high areas were mainly distributed in the central， northeast， and southeast parts.
The efficacy of groundwater pollution risk control is often limited by a lack of information on spatial correlations between groundwater vulnerabilities and pollution sources. To overcome this limitation， a method was established to reveal the spatial correlations between groundwater vulnerabilities and industrial pollution sources in an industrialized city of Guangdong Province， China， using a combination of genetic algorithm （GA）， back propagation neural network （BPNN）， kernel density estimation （KDE）， and bivariate local Moran’s I （BLMI）. The subjectivity in the indicator weighting of a DRASTICL model was successfully reduced by GA-BPNN， and then the groundwater vulnerability map was created by the GA-BPNN-DRASTICL model. A spatial distribution map of the industrial pollution source distribution was produced by KDE. The spatial clustering map between groundwater vulnerabilities and industrial pollution sources was generated by BLMI， explicitly showing their distribution characteristics and implying that specific measures should be taken to control risks of groundwater pollution in different parts of the study area. The results showed that the best accuracy of the GA-BPNN algorithm was obtained， with the training function of trainlm， the neuron number of 6 in the hidden layer， the learning rate of 0.1， the population size of 40， the crossover rate of 0.6， and the mutation rate of 0.01. The best indicator weights for depth to the water table， net recharge， aquifer medium， soil medium， topography， impact of vadose zone， hydraulic conductivity， and land use were 2.84， 5.27， 0.84， 2.20， 2.36， 6.58， 1.21， and 6.70， respectively. The high and very high vulnerability classes were concentrated in the central and southern parts of the study area. The largest hotspot of the industrial pollution sources was located in the midwest part， and the high-high areas were mainly distributed in the central， northeast， and southeast parts.
2025, 43(12): 121-128.
doi: 10.13205/j.hjgc.202512014
Abstract:
Wastewater treatment is an important source of methane （CH4） and nitrous oxide （N2O） emissions. The method for simultaneously enhancing pollutant removal and reducing greenhouse gas emissions needs to be investigated. Ferric oxide， known as magnetic iron oxide， has the characteristics of better electrical conductivity and magnetism， and it has been gradually paid attention to in pollutants treatment. In this study， a monitoring device for nitrogen removal and greenhouse gas emissions was constructed to investigate the short-term biological effects of ferroferric oxide nanoparticles， and explore the nitrogen removal performance and greenhouse gas emission characteristics of denitrification and partial denitrification-anaerobic ammoxidation （PD/A） reactions. The results showed that， in the treatment of actual domestic sewage， the addition of ferroferric oxide nanoparticles increased the NO-3-N removal rate of denitrification by 2.13 times， and N2O emissions were reduced by 25%. Furthermore， the addition of ferroferric oxide nanoparticles increased the NO-3-N removal rate of PD/A reactions by 1.53 times， while decreasing CH4 and N2O emissions by 71.4% and 33.3%， respectively. A similar effect was observed in the treatment of simulated wastewater prepared with sodium acetate. As an external carbon source， sodium acetate is widely used in sewage treatment plants， which has a low GHG emission factor while increasing the total nitrogen removal rate. The addition of ferroferric oxide nanoparticles proved to be effective in achieving efficient nitrogen removal in the presence of limited carbon sources， significantly increased NO-3-N removal rate， and reduced CH4 and N2O emissions. These effects are likely attributed to the promotion of ferroferric oxide nanoparticles on the activity of key functional enzymes and their ability to facilitate electron transfer.
Wastewater treatment is an important source of methane （CH4） and nitrous oxide （N2O） emissions. The method for simultaneously enhancing pollutant removal and reducing greenhouse gas emissions needs to be investigated. Ferric oxide， known as magnetic iron oxide， has the characteristics of better electrical conductivity and magnetism， and it has been gradually paid attention to in pollutants treatment. In this study， a monitoring device for nitrogen removal and greenhouse gas emissions was constructed to investigate the short-term biological effects of ferroferric oxide nanoparticles， and explore the nitrogen removal performance and greenhouse gas emission characteristics of denitrification and partial denitrification-anaerobic ammoxidation （PD/A） reactions. The results showed that， in the treatment of actual domestic sewage， the addition of ferroferric oxide nanoparticles increased the NO-3-N removal rate of denitrification by 2.13 times， and N2O emissions were reduced by 25%. Furthermore， the addition of ferroferric oxide nanoparticles increased the NO-3-N removal rate of PD/A reactions by 1.53 times， while decreasing CH4 and N2O emissions by 71.4% and 33.3%， respectively. A similar effect was observed in the treatment of simulated wastewater prepared with sodium acetate. As an external carbon source， sodium acetate is widely used in sewage treatment plants， which has a low GHG emission factor while increasing the total nitrogen removal rate. The addition of ferroferric oxide nanoparticles proved to be effective in achieving efficient nitrogen removal in the presence of limited carbon sources， significantly increased NO-3-N removal rate， and reduced CH4 and N2O emissions. These effects are likely attributed to the promotion of ferroferric oxide nanoparticles on the activity of key functional enzymes and their ability to facilitate electron transfer.
2025, 43(12): 129-140.
doi: 10.13205/j.hjgc.202512015
Abstract:
To address the frequent occurrence of PM2.5 and O3 compound pollution， this study explored the spatiotemporal distribution characteristics of PM2.5 and O3 compound pollution and its correlation with the meteorological factors in Fenwei Plain， which is one of the key areas for prevention and control over air pollution in China. It was found that： 1） from 2014 to 2021， the average annual concentration of PM2.5 in Fenwei Plain showed a downward trend， with PM2.5 pollutants concentrated in Xi'an， Luoyang， and Linfen； O3 pollution showed a trend of fluctuation and rise， with O3 pollutants concentrated in the southeastern cities of the Plain， including Luoyang， Sanmenxia， Yuncheng， and Linfen. The daily variations of PM2.5 concentration in the Plain showed a "U-shaped" pattern， while the daily means' change of O3 concentration showed an "inverted U-shaped" characteristic， with the concentration reaching the highest value at 16：00. 2） during the study period， both PM2.5-O3 compound-polluted and single PM2.5 polluted days showed a decreasing trend， with the co-polluted days concentrated in April to October of a year； the spatial distribution of the co-polluted days showed a decreasing trend from the southeast corner to both sides of the Plain， and PM2.5 and O3 pollution in the Plain showcased a positive correlation coefficient in summer and a negative one in winter. 3） PM2.5-O3 compound pollution occurred easily in the following conditions： the air temperature of 13.0 to 29.7 ℃， the relative humidity of 35% to 75%， eastern wind， and the wind speed of 1.5 to 3.1 m/s. 4） backward trajectory cluster analysis demonstrated that the compound pollution in the southwest cities of Fenwei Plain was significantly affected by the short-path air flows coming from the southeast； the compound pollution in the central cities mainly came from some regions of Xinjiang and Inner Mongolia； and the compound pollution in the southeastern and northern cities mainly came from the northwestern and northeastern long-distance transports of pollutants.
To address the frequent occurrence of PM2.5 and O3 compound pollution， this study explored the spatiotemporal distribution characteristics of PM2.5 and O3 compound pollution and its correlation with the meteorological factors in Fenwei Plain， which is one of the key areas for prevention and control over air pollution in China. It was found that： 1） from 2014 to 2021， the average annual concentration of PM2.5 in Fenwei Plain showed a downward trend， with PM2.5 pollutants concentrated in Xi'an， Luoyang， and Linfen； O3 pollution showed a trend of fluctuation and rise， with O3 pollutants concentrated in the southeastern cities of the Plain， including Luoyang， Sanmenxia， Yuncheng， and Linfen. The daily variations of PM2.5 concentration in the Plain showed a "U-shaped" pattern， while the daily means' change of O3 concentration showed an "inverted U-shaped" characteristic， with the concentration reaching the highest value at 16：00. 2） during the study period， both PM2.5-O3 compound-polluted and single PM2.5 polluted days showed a decreasing trend， with the co-polluted days concentrated in April to October of a year； the spatial distribution of the co-polluted days showed a decreasing trend from the southeast corner to both sides of the Plain， and PM2.5 and O3 pollution in the Plain showcased a positive correlation coefficient in summer and a negative one in winter. 3） PM2.5-O3 compound pollution occurred easily in the following conditions： the air temperature of 13.0 to 29.7 ℃， the relative humidity of 35% to 75%， eastern wind， and the wind speed of 1.5 to 3.1 m/s. 4） backward trajectory cluster analysis demonstrated that the compound pollution in the southwest cities of Fenwei Plain was significantly affected by the short-path air flows coming from the southeast； the compound pollution in the central cities mainly came from some regions of Xinjiang and Inner Mongolia； and the compound pollution in the southeastern and northern cities mainly came from the northwestern and northeastern long-distance transports of pollutants.
2025, 43(12): 141-152.
doi: 10.13205/j.hjgc.202512016
Abstract:
The Yellow River Basin is an important ecological barrier and energy base in China. Promoting carbon emissions reduction in the Yellow River Basin is of great significance for China to achieve the Dual Carbon Goals. Utilizing energy consumption， population distribution data， and nighttime light data spanning 2000 to 2022 in the Yellow River Basin， this study estimated CO2 emissions for prefecture-level administrative regions （referred to as cities）. The spatiotemporal pattern of per capita CO2 emissions was analyzed using exploratory spatiotemporal data analysis （ESTDA） methods. Additionally， an extended STIRPAT model and panel data regression techniques were employed to identify factors influencing per capita CO2 emissions in these cities. The findings indicate： 1） Per capita CO2 emissions in the Yellow River Basin exhibit a spatial pattern with higher emissions in the middle reaches， and lower emissions in the upstream and downstream regions. 2） From 2000 to 2022， the spatiotemporal dynamics of per capita CO2 emissions in cities of the Yellow River Basin generally remained stable with some local changes. The overall stability was reflected in the fact that， from 2000 to 2022， the spatiotemporal cohesion rate of per capita CO2 emissions was 81.5%， with the dominant proportion not experiencing a change in the associated patterns. Local dynamics were reflected in the shift in spatial association structures of per capita CO2 emissions in resource-based cities and some economically developed cities. Among the two types of spatiotemporal transitions， Type 1 （10.8%） > Type 2 （7.7%）， indicating that some cities in the Yellow River Basin have experienced spatial association lock-in for per capita CO2 emissions. 3） The spatial panel regression results show that economic growth is positively correlated with per capita CO2 emissions in the Yellow River Basin. Urbanization level， population size， the share of the tertiary industry in GDP， the share of fixed asset investment in GDP， and the share of total import and export value in GDP are negatively correlated with per capita CO2 emissions. The relationship between the secondary industry’s value-added share in GDP and per capita CO2 emissions remains uncertain. Additionally， the widespread spatial interaction among cities in the Yellow River Basin and the significant spatial lag effect positively promoted per capita CO2 emissions. Therefore， when formulating low-carbon development strategies， regional differences and spatial interactions should be considered， and each province or region should develop targeted emission reduction policies based on its specific characteristics. The research findings provide decision-making references for deep cooperation and coordinated emission reduction across various regions of the Yellow River Basin， helping to promote ecological protection and high-quality development in the basin.
The Yellow River Basin is an important ecological barrier and energy base in China. Promoting carbon emissions reduction in the Yellow River Basin is of great significance for China to achieve the Dual Carbon Goals. Utilizing energy consumption， population distribution data， and nighttime light data spanning 2000 to 2022 in the Yellow River Basin， this study estimated CO2 emissions for prefecture-level administrative regions （referred to as cities）. The spatiotemporal pattern of per capita CO2 emissions was analyzed using exploratory spatiotemporal data analysis （ESTDA） methods. Additionally， an extended STIRPAT model and panel data regression techniques were employed to identify factors influencing per capita CO2 emissions in these cities. The findings indicate： 1） Per capita CO2 emissions in the Yellow River Basin exhibit a spatial pattern with higher emissions in the middle reaches， and lower emissions in the upstream and downstream regions. 2） From 2000 to 2022， the spatiotemporal dynamics of per capita CO2 emissions in cities of the Yellow River Basin generally remained stable with some local changes. The overall stability was reflected in the fact that， from 2000 to 2022， the spatiotemporal cohesion rate of per capita CO2 emissions was 81.5%， with the dominant proportion not experiencing a change in the associated patterns. Local dynamics were reflected in the shift in spatial association structures of per capita CO2 emissions in resource-based cities and some economically developed cities. Among the two types of spatiotemporal transitions， Type 1 （10.8%） > Type 2 （7.7%）， indicating that some cities in the Yellow River Basin have experienced spatial association lock-in for per capita CO2 emissions. 3） The spatial panel regression results show that economic growth is positively correlated with per capita CO2 emissions in the Yellow River Basin. Urbanization level， population size， the share of the tertiary industry in GDP， the share of fixed asset investment in GDP， and the share of total import and export value in GDP are negatively correlated with per capita CO2 emissions. The relationship between the secondary industry’s value-added share in GDP and per capita CO2 emissions remains uncertain. Additionally， the widespread spatial interaction among cities in the Yellow River Basin and the significant spatial lag effect positively promoted per capita CO2 emissions. Therefore， when formulating low-carbon development strategies， regional differences and spatial interactions should be considered， and each province or region should develop targeted emission reduction policies based on its specific characteristics. The research findings provide decision-making references for deep cooperation and coordinated emission reduction across various regions of the Yellow River Basin， helping to promote ecological protection and high-quality development in the basin.
2025, 43(12): 153-160.
doi: 10.13205/j.hjgc.202512017
Abstract:
This paper numerically simulated the collaborative removal of particulate matter of a 2-tandem FGD absorber system， established a droplet capture model based on diffusion， interception， and inertial impact effects， and used the methods of segmentation， ME sub-model， and whole absorber calculation， to analyze the gas-liquid-solid three-phase flow and capture efficiencies in the pre-absorber and absorber. The results showed that the pre-absorber and absorber could effectively remove dust from the flue gas. When the dust concentration at the outlet of the dust collector was 50 mg/m3， the dust concentration at the absorber outlet could be reduced to 2.8 mg/m3. The solid concentration at the outlet of the absorber caused by escaping gypsum was 1.83 mg/m3； through comprehensive calculation， it was proved to meet the ultra-low emission standard. The liquid droplets had a great impact on the solid particle emission， and an efficient ME value was needed to ensure the emission level. This paper provides a reference for the optimization design and operation of wet desulfurization systems.
This paper numerically simulated the collaborative removal of particulate matter of a 2-tandem FGD absorber system， established a droplet capture model based on diffusion， interception， and inertial impact effects， and used the methods of segmentation， ME sub-model， and whole absorber calculation， to analyze the gas-liquid-solid three-phase flow and capture efficiencies in the pre-absorber and absorber. The results showed that the pre-absorber and absorber could effectively remove dust from the flue gas. When the dust concentration at the outlet of the dust collector was 50 mg/m3， the dust concentration at the absorber outlet could be reduced to 2.8 mg/m3. The solid concentration at the outlet of the absorber caused by escaping gypsum was 1.83 mg/m3； through comprehensive calculation， it was proved to meet the ultra-low emission standard. The liquid droplets had a great impact on the solid particle emission， and an efficient ME value was needed to ensure the emission level. This paper provides a reference for the optimization design and operation of wet desulfurization systems.
2025, 43(12): 161-168.
doi: 10.13205/j.hjgc.202512018
Abstract:
During the heating season in severe cold region， there is often a high concentration of PM2.5 pollution indoors. Accurately predicting indoor PM2.5 pollution levels is crucial for developing effective purification measures. The indoor PM2.5 concentrations and window-opening behaviors of 7 residential buildings in a severe cold region during the heating season were analyzed. The PM2.5 concentration exceeded the standard level by 35.94% in the living room， and the daily average duration of window-opening was relatively short. At the same time， the Informer model was trained using indoor PM2.5 concentration historical data， with the duration of window-opening as a feature input to achieve the prediction of indoor PM2.5 concentration. By comparing the prediction performance of the model under different feature input strategies， input step sizes， prediction ranges， and adding rolling prediction， the optimal configuration of the model to achieve high-precision prediction was determined after comprehensive evaluation. The model was capable of predicting indoor PM2.5 concentrations for a future period， and the evaluation metrics for its prediction performance were： MAE of 10.70 μg/m3， RMSE of 13.75 μg/m3， and R2 of 0.795. Compared to the TCN-LSTM model， the R2 of the Informer model increased by 13.9%. The optimized model presents a more accurate prediction effect.
During the heating season in severe cold region， there is often a high concentration of PM2.5 pollution indoors. Accurately predicting indoor PM2.5 pollution levels is crucial for developing effective purification measures. The indoor PM2.5 concentrations and window-opening behaviors of 7 residential buildings in a severe cold region during the heating season were analyzed. The PM2.5 concentration exceeded the standard level by 35.94% in the living room， and the daily average duration of window-opening was relatively short. At the same time， the Informer model was trained using indoor PM2.5 concentration historical data， with the duration of window-opening as a feature input to achieve the prediction of indoor PM2.5 concentration. By comparing the prediction performance of the model under different feature input strategies， input step sizes， prediction ranges， and adding rolling prediction， the optimal configuration of the model to achieve high-precision prediction was determined after comprehensive evaluation. The model was capable of predicting indoor PM2.5 concentrations for a future period， and the evaluation metrics for its prediction performance were： MAE of 10.70 μg/m3， RMSE of 13.75 μg/m3， and R2 of 0.795. Compared to the TCN-LSTM model， the R2 of the Informer model increased by 13.9%. The optimized model presents a more accurate prediction effect.
2025, 43(12): 169-177.
doi: 10.13205/j.hjgc.202512019
Abstract:
The low-energy， high-efficiency control of fugitive emissions from high-temperature， dust-laden gas generated during the capping and airing processes before coke pushing into coke ovens， is a pressing challenge that needs to be addressed for achieving the ultra-low emissions in the coking industry. This study was focused on a specific coking plant equipped with a low-efficiency， waste gas collection system for ascension pipes， to address the issues of low collection efficiency and high energy consumption in its existing ascension pipe waste gas collection system. A numerical simulation-based method was employed to analyze the diffusion patterns of waste gas emissions from the ascension pipes and optimize the waste gas collection system to improve dust collection efficiency. Based on the results， an automated control system based on programmable logic controller （PLC） was developed. The results indicated that the currently operational waste gas collection hood was 4 meters from the ascension pipe's central axis. The velocity of the hood decreased dramatically， causing the dust to disperse after reaching the furnace hood， resulting in a collecting efficiency of only 40%. Adding a roof collection chamber ensured the emergency venting function while buffering gas flow velocity. Positioning the collection hood on one side of the roof collection chamber， and decreasing the distance between the hood and the central axis of the ascension pipe to 1.6 meters， enabled 100% dust collection from the ascension pipe at an airflow of 3.3×104 m3/h. In Work Mode 1， dust collection effectiveness got improved with the growing of waste air volume of the collecting hood. Dust was effectively kept from spreading sideways in Work Mode 2 by positioning the collection hood on both ends of the ascension pipe. Dust was entirely collected at an exhaust rate of 4×104 m3/h， except for ascension pipes 1-4. The PLC system associated with the coke oven pushing schedule provides entirely automated intelligent control of the gas collection system. The airflow rate of the system was reduced from 8×10⁵ m3/h to 1.6×105 m3/h， resulting in an 80% reduction in energy consumption. This research provides insights for the efficient， low-energy control of waste gas dispersion from ascension pipes and offers a practical reference for addressing the ultra-low emissions challenge in the coke industry.
The low-energy， high-efficiency control of fugitive emissions from high-temperature， dust-laden gas generated during the capping and airing processes before coke pushing into coke ovens， is a pressing challenge that needs to be addressed for achieving the ultra-low emissions in the coking industry. This study was focused on a specific coking plant equipped with a low-efficiency， waste gas collection system for ascension pipes， to address the issues of low collection efficiency and high energy consumption in its existing ascension pipe waste gas collection system. A numerical simulation-based method was employed to analyze the diffusion patterns of waste gas emissions from the ascension pipes and optimize the waste gas collection system to improve dust collection efficiency. Based on the results， an automated control system based on programmable logic controller （PLC） was developed. The results indicated that the currently operational waste gas collection hood was 4 meters from the ascension pipe's central axis. The velocity of the hood decreased dramatically， causing the dust to disperse after reaching the furnace hood， resulting in a collecting efficiency of only 40%. Adding a roof collection chamber ensured the emergency venting function while buffering gas flow velocity. Positioning the collection hood on one side of the roof collection chamber， and decreasing the distance between the hood and the central axis of the ascension pipe to 1.6 meters， enabled 100% dust collection from the ascension pipe at an airflow of 3.3×104 m3/h. In Work Mode 1， dust collection effectiveness got improved with the growing of waste air volume of the collecting hood. Dust was effectively kept from spreading sideways in Work Mode 2 by positioning the collection hood on both ends of the ascension pipe. Dust was entirely collected at an exhaust rate of 4×104 m3/h， except for ascension pipes 1-4. The PLC system associated with the coke oven pushing schedule provides entirely automated intelligent control of the gas collection system. The airflow rate of the system was reduced from 8×10⁵ m3/h to 1.6×105 m3/h， resulting in an 80% reduction in energy consumption. This research provides insights for the efficient， low-energy control of waste gas dispersion from ascension pipes and offers a practical reference for addressing the ultra-low emissions challenge in the coke industry.
2025, 43(12): 178-185.
doi: 10.13205/j.hjgc.202512020
Abstract:
The single model for NO x generation in thermal power plants can only be trained based on partial data features， failing to fully explore and utilize the potential value of the data. To effectively predict NO x emissions from coal-fired power plants， this paper proposed a feature selection method based on Pearson correlation analysis and an extreme gradient boosting （XGBoost） algorithm optimized by particle swarm optimization （PSO）. Firstly， Pearson correlation analysis was used to calculate the correlation coefficients of various features and conduct feature selection. Secondly， the particle swarm optimization algorithm was employed to fine-tune the hyperparameters of the XGBoost model， ensuring its robustness and generalization ability under different operating conditions. Finally， the prediction results were compared with those of other machine learning algorithms for verification. Predictions were conducted based on the actual data of a boiler unit. The mean absolute percentage error （MAPE） between the predicted and actual values was 0.93%， the root mean square error （RMSE） was 5.959， the mean absolute error （MAE） was 3.564， and the coefficient of determination （R2） was 0.97. The results indicate that the improved model outperforms other machine learning algorithms in predicting NO x emissions， significantly reducing prediction errors and greatly enhancing the accuracy and practicality of the model.
The single model for NO x generation in thermal power plants can only be trained based on partial data features， failing to fully explore and utilize the potential value of the data. To effectively predict NO x emissions from coal-fired power plants， this paper proposed a feature selection method based on Pearson correlation analysis and an extreme gradient boosting （XGBoost） algorithm optimized by particle swarm optimization （PSO）. Firstly， Pearson correlation analysis was used to calculate the correlation coefficients of various features and conduct feature selection. Secondly， the particle swarm optimization algorithm was employed to fine-tune the hyperparameters of the XGBoost model， ensuring its robustness and generalization ability under different operating conditions. Finally， the prediction results were compared with those of other machine learning algorithms for verification. Predictions were conducted based on the actual data of a boiler unit. The mean absolute percentage error （MAPE） between the predicted and actual values was 0.93%， the root mean square error （RMSE） was 5.959， the mean absolute error （MAE） was 3.564， and the coefficient of determination （R2） was 0.97. The results indicate that the improved model outperforms other machine learning algorithms in predicting NO x emissions， significantly reducing prediction errors and greatly enhancing the accuracy and practicality of the model.
2025, 43(12): 186-196.
doi: 10.13205/j.hjgc.202512021
Abstract:
Atmospheric PM2.5 pollution is one of the typical environmental problems in Beijing， and the negative impact of bacterial communities of PM2.5 on human health are attracting increasing public attention. In this study， a one-year sampling of atmospheric PM2.5 was carried out at a typical atmospheric monitoring station in Beijing urban area， and samples were collected from four seasons of regular weather and haze， rainfall， snowfall， sandstorms， and dust storms， etc. Seasonal changes of the bacterial community structure in PM2.5 were investigated by combining molecular biology and multivariate statistical methods. 43 phyla and 1344 genera were found in PM2.5， and the dominant and characteristic genera varied in different seasons and typical weather， with norank_f__norank_o__Chloroplast and norank_f__Mitochondria in spring， Acidibacter and Paracoccus in summer， Ammoniphilus and unclassified_k__norank_d__Bacteria in autumn， and Sphingomonas and Pseudomonas in winter. The characteristic genus was Diaphorobacter in hazy weather， Delftia in rainy weather， Terrisporobacter in snowy weather， and norank_f__Cytophagaceae in dusty weather. The results of the non-metric multidimensional scaling （NMDS） analysis showed that the atmospheric bacterial community was more stable in summer and that typical weather such as rainfall， snowfall， haze， and dust storms in other seasons would cause greater perturbations to the bacterial community. Based on the functional prediction of BugBase， it was found that the atmospheric microorganisms are mainly aerobic and gram-negative bacteria. Acidibacter is the most abundant potential pathogenic bacterium in the four seasons， and the potential pathogenic risk of atmospheric microorganisms is greater in summer. This study are important for the development of air quality management strategies under different seasons and weather conditions.
Atmospheric PM2.5 pollution is one of the typical environmental problems in Beijing， and the negative impact of bacterial communities of PM2.5 on human health are attracting increasing public attention. In this study， a one-year sampling of atmospheric PM2.5 was carried out at a typical atmospheric monitoring station in Beijing urban area， and samples were collected from four seasons of regular weather and haze， rainfall， snowfall， sandstorms， and dust storms， etc. Seasonal changes of the bacterial community structure in PM2.5 were investigated by combining molecular biology and multivariate statistical methods. 43 phyla and 1344 genera were found in PM2.5， and the dominant and characteristic genera varied in different seasons and typical weather， with norank_f__norank_o__Chloroplast and norank_f__Mitochondria in spring， Acidibacter and Paracoccus in summer， Ammoniphilus and unclassified_k__norank_d__Bacteria in autumn， and Sphingomonas and Pseudomonas in winter. The characteristic genus was Diaphorobacter in hazy weather， Delftia in rainy weather， Terrisporobacter in snowy weather， and norank_f__Cytophagaceae in dusty weather. The results of the non-metric multidimensional scaling （NMDS） analysis showed that the atmospheric bacterial community was more stable in summer and that typical weather such as rainfall， snowfall， haze， and dust storms in other seasons would cause greater perturbations to the bacterial community. Based on the functional prediction of BugBase， it was found that the atmospheric microorganisms are mainly aerobic and gram-negative bacteria. Acidibacter is the most abundant potential pathogenic bacterium in the four seasons， and the potential pathogenic risk of atmospheric microorganisms is greater in summer. This study are important for the development of air quality management strategies under different seasons and weather conditions.
2025, 43(12): 197-212.
doi: 10.13205/j.hjgc.202512022
Abstract:
Nitrogen dioxide （NO2） is a significant pollutant in the troposphere， posing substantial threats to the environment and human health. Accurately predicting NO2 concentrations is crucial for air pollution management， policy formulation， and public health protection. This study focused on Shanghai as the research area and proposed a novel hybrid model integrating complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and the Informer deep learning model， to predict the daily average concentration of NO2. First， CEEMDAN was employed to decompose the original NO2 time series， and a grey wolf optimization （GWO） algorithm combined with minimum sample entropy （SE） was utilized as a fitness function to optimize the key parameters of CEEMDAN. This approach effectively reduced random fluctuations in the data and extracted intrinsic mode functions （IMFs）. Subsequently， the Pearson correlation coefficient and Spearman rank correlation coefficient were used to analyze the association strength among various pollutants， allowing for the selection of optimal features for each IMF and the construction of a high-coupling feature matrix. These features were then input into an optimized Informer model based on the Transformer architecture for encoding and modeling. By employing mechanisms such as Multi-head ProbSparse self-attention and attention distillation， the model's prediction accuracy and efficiency got enhanced. Finally， the predicted results of each IMF were summed to reconstruct the final predicted value. Experimental results indicated that the proposed model achieved an average absolute error （MAE） of 5.027 and a root mean square error （RMSE） of 6.818， demonstrating a significant improvement in prediction accuracy， compared to the original Informer model and other benchmark models including LSTM， GRU， Transformer， and SVR. The innovations in data processing， feature engineering， and model architecture presented in this study offer a more precise method for NO2 concentration prediction， with broad application prospects that can provide robust support for regional air quality forecasting， early warning systems， and pollution control strategies.
Nitrogen dioxide （NO2） is a significant pollutant in the troposphere， posing substantial threats to the environment and human health. Accurately predicting NO2 concentrations is crucial for air pollution management， policy formulation， and public health protection. This study focused on Shanghai as the research area and proposed a novel hybrid model integrating complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and the Informer deep learning model， to predict the daily average concentration of NO2. First， CEEMDAN was employed to decompose the original NO2 time series， and a grey wolf optimization （GWO） algorithm combined with minimum sample entropy （SE） was utilized as a fitness function to optimize the key parameters of CEEMDAN. This approach effectively reduced random fluctuations in the data and extracted intrinsic mode functions （IMFs）. Subsequently， the Pearson correlation coefficient and Spearman rank correlation coefficient were used to analyze the association strength among various pollutants， allowing for the selection of optimal features for each IMF and the construction of a high-coupling feature matrix. These features were then input into an optimized Informer model based on the Transformer architecture for encoding and modeling. By employing mechanisms such as Multi-head ProbSparse self-attention and attention distillation， the model's prediction accuracy and efficiency got enhanced. Finally， the predicted results of each IMF were summed to reconstruct the final predicted value. Experimental results indicated that the proposed model achieved an average absolute error （MAE） of 5.027 and a root mean square error （RMSE） of 6.818， demonstrating a significant improvement in prediction accuracy， compared to the original Informer model and other benchmark models including LSTM， GRU， Transformer， and SVR. The innovations in data processing， feature engineering， and model architecture presented in this study offer a more precise method for NO2 concentration prediction， with broad application prospects that can provide robust support for regional air quality forecasting， early warning systems， and pollution control strategies.
2025, 43(12): 213-221.
doi: 10.13205/j.hjgc.202512023
Abstract:
To explore the research hotspots and development trends of the research field on environmental pollution caused by drilling waste， all the related literature published during the period from 1994 to 2024 was retrieved based on the core collection database of Web of Science. Based on the CiteSpace software， the bibliometric and visual analysis was conducted for annual publication volume， publishing countries， publishing institutions， publishing author groups， keyword clustering， and keyword emergence， and then the potential future research trends were concluded， and the important research directions were finally proposed. The results showed that the number of published articles exhibited an increasing trend in recent years. Among them， China and the United States were the top two productive countries with the highest number of publications. Generally， the cooperation among different research institutions， research groups appeared to be relatively limited， and the research duration time of each research institution or group was relatively short. Moreover， the results of keyword clustering， burst term analysis， and keyword timeline analysis indicated that the research on drilling waste pollution have been deepened gradually， and the research focuses are shifting from single pollution （e.g.， heavy metal） to multi-pollution （e.g.， heavy metals， petroleum hydrocarbons and chemical agents）， from biological remediation of contaminated sites by drilling wastes， to the treatment and disposal of drilling wastes before discharging， and from environmental and ecological risks of soil， water and toxic effects of plants and animals， to human health risks， and the eco-friendly drilling fluid will be continuously researched and developed. Finally， four major future research directions were put forward in this field including： 1） the integrative pollution risk assessment of multiple pollutants of drilling wastes and multi-scale action mechanisms； 2） the research and development of multi-path synergistic treatment and disposal of drilling wastes； 3） the diffusion in the environment and transportation in food chains （networks） of drilling wastes and their human health risk assessment； 4） the applications of biodegradable drilling fluid.
To explore the research hotspots and development trends of the research field on environmental pollution caused by drilling waste， all the related literature published during the period from 1994 to 2024 was retrieved based on the core collection database of Web of Science. Based on the CiteSpace software， the bibliometric and visual analysis was conducted for annual publication volume， publishing countries， publishing institutions， publishing author groups， keyword clustering， and keyword emergence， and then the potential future research trends were concluded， and the important research directions were finally proposed. The results showed that the number of published articles exhibited an increasing trend in recent years. Among them， China and the United States were the top two productive countries with the highest number of publications. Generally， the cooperation among different research institutions， research groups appeared to be relatively limited， and the research duration time of each research institution or group was relatively short. Moreover， the results of keyword clustering， burst term analysis， and keyword timeline analysis indicated that the research on drilling waste pollution have been deepened gradually， and the research focuses are shifting from single pollution （e.g.， heavy metal） to multi-pollution （e.g.， heavy metals， petroleum hydrocarbons and chemical agents）， from biological remediation of contaminated sites by drilling wastes， to the treatment and disposal of drilling wastes before discharging， and from environmental and ecological risks of soil， water and toxic effects of plants and animals， to human health risks， and the eco-friendly drilling fluid will be continuously researched and developed. Finally， four major future research directions were put forward in this field including： 1） the integrative pollution risk assessment of multiple pollutants of drilling wastes and multi-scale action mechanisms； 2） the research and development of multi-path synergistic treatment and disposal of drilling wastes； 3） the diffusion in the environment and transportation in food chains （networks） of drilling wastes and their human health risk assessment； 4） the applications of biodegradable drilling fluid.
2025, 43(12): 222-236.
doi: 10.13205/j.hjgc.202512024
Abstract:
As China and its industries accelerate to achieve net zero carbon emission， carbon capture and storage （CCS） technology will play a key role， however， the development of CCS projects is facing many challenges， the development of a carbon reduction accounting method applicable to CCS projects in China is of great significance for incorporating CCS into the CCER carbon trading system and obtaining carbon subsidies. This paper focused on the whole process carbon dioxide capture geological storage （CCS） projects， learned from global existing methodology experience， proposed a set of carbon emission reduction accounting methodology for CCS projects， clarified the application conditions of this method and the argumentation of additionality， presented the boundary and emission source of CCS projects in the whole process， the identification of project baseline scenarios and the calculation method of baseline emissions， and put forward the calculation method of carbon emission reduction of CCS projects in detail， clarified the project counting period and data monitoring and management requirements. It proposed a detailed calculation method for carbon emission reduction in CCS projects， which can provides support for the accurate calculation of net emission reduction in the whole process of CCS projects in various scenarios. China should accelerate the establishment of the CCS project carbon emissions accounting system and incorporate CCS projects into its carbon emission market as soon as possible， to promote the rapid development of CCS projects and help China achieve the carbon peaking and carbon neutrality goals at an earlier date.
As China and its industries accelerate to achieve net zero carbon emission， carbon capture and storage （CCS） technology will play a key role， however， the development of CCS projects is facing many challenges， the development of a carbon reduction accounting method applicable to CCS projects in China is of great significance for incorporating CCS into the CCER carbon trading system and obtaining carbon subsidies. This paper focused on the whole process carbon dioxide capture geological storage （CCS） projects， learned from global existing methodology experience， proposed a set of carbon emission reduction accounting methodology for CCS projects， clarified the application conditions of this method and the argumentation of additionality， presented the boundary and emission source of CCS projects in the whole process， the identification of project baseline scenarios and the calculation method of baseline emissions， and put forward the calculation method of carbon emission reduction of CCS projects in detail， clarified the project counting period and data monitoring and management requirements. It proposed a detailed calculation method for carbon emission reduction in CCS projects， which can provides support for the accurate calculation of net emission reduction in the whole process of CCS projects in various scenarios. China should accelerate the establishment of the CCS project carbon emissions accounting system and incorporate CCS projects into its carbon emission market as soon as possible， to promote the rapid development of CCS projects and help China achieve the carbon peaking and carbon neutrality goals at an earlier date.
2025, 43(12): 237-246.
doi: 10.13205/j.hjgc.202512025
Abstract:
Land use/cover （LULC） change is an important factor affecting carbon storage in terrestrial ecosystems. Quantitative analysis of the impact of LULC change on carbon storage is of great significance for exploring sustainable urban development and improving the value of ecosystem services. Taking Lvliang in Shanxi Province as an example， the GeoSoS-FLUS model was used to simulate the LULC conditions of Lvliang in 2030 under three scenarios， and the carbon storage of Lvliang from 2010 to 2030 was estimated based on the InVEST model. At the same time， the impact of LULC change on carbon storage under each scenario was analyzed and predicted. The results showed that： 1） cultivated land， forest land and grassland plays an important role in the carbon storage of Lvliang； 2） from 2010 to 2020， the conversion of cultivated land， forest land and grassland into construction land was an important feature of LULC change， resulting in a large amount of carbon loss； 3） from 2020 to 2030， under the natural development scenario， construction land will further expand and carbon storage will continue to decline； under the cultivated land protection scenario， cultivated land is prohibited from being converted into other land types， and carbon storage will improve； under the ecological priority scenario， carbon storage will increase significantly due to the strengthening of ecological land protection， but cultivated land will inevitably decrease.
Land use/cover （LULC） change is an important factor affecting carbon storage in terrestrial ecosystems. Quantitative analysis of the impact of LULC change on carbon storage is of great significance for exploring sustainable urban development and improving the value of ecosystem services. Taking Lvliang in Shanxi Province as an example， the GeoSoS-FLUS model was used to simulate the LULC conditions of Lvliang in 2030 under three scenarios， and the carbon storage of Lvliang from 2010 to 2030 was estimated based on the InVEST model. At the same time， the impact of LULC change on carbon storage under each scenario was analyzed and predicted. The results showed that： 1） cultivated land， forest land and grassland plays an important role in the carbon storage of Lvliang； 2） from 2010 to 2020， the conversion of cultivated land， forest land and grassland into construction land was an important feature of LULC change， resulting in a large amount of carbon loss； 3） from 2020 to 2030， under the natural development scenario， construction land will further expand and carbon storage will continue to decline； under the cultivated land protection scenario， cultivated land is prohibited from being converted into other land types， and carbon storage will improve； under the ecological priority scenario， carbon storage will increase significantly due to the strengthening of ecological land protection， but cultivated land will inevitably decrease.
2025, 43(12): 247-256.
doi: 10.13205/j.hjgc.202512026
Abstract:
As an important coal production base in China， the Yellow River Basin’s carbon emissions have been rising since 2000， posing a challenge to China’s Dual-Carbon Goals. As a high-energy-consuming infrastructure， wastewater treatment plants are important sources of greenhouse gas emissions. Based on the case of a sewage treatment plant in Inner Mongolia， this study proposed the idea of building a carbon management platform and discussed the effect of digital carbon management according to the carbon emission accounting standards and methods of IPCC， and the operation and management rules of sewage treatment. The results showed that the platform can accurately identify the main carbon emission sources， quantitatively evaluate the emission reduction effect， and accurately identify the main carbon emission sources， of which the carbon emission of the sewage treatment system accounts for about 66% to 75%， and the energy consumption of the aeration link can be reduced by 28% through optimal control. For the first time， this study combined the IPCC carbon emission accounting standard with digital technology to build a digital carbon management platform， which realized the comprehensive monitoring， accurate accounting， and optimal regulation of carbon emissions. It provides quantifiable management tools and practical references for pollution reduction and carbon reduction in sewage treatment plants， and has important demonstration significance for promoting the green and low-carbon transformation of the whole industry.
As an important coal production base in China， the Yellow River Basin’s carbon emissions have been rising since 2000， posing a challenge to China’s Dual-Carbon Goals. As a high-energy-consuming infrastructure， wastewater treatment plants are important sources of greenhouse gas emissions. Based on the case of a sewage treatment plant in Inner Mongolia， this study proposed the idea of building a carbon management platform and discussed the effect of digital carbon management according to the carbon emission accounting standards and methods of IPCC， and the operation and management rules of sewage treatment. The results showed that the platform can accurately identify the main carbon emission sources， quantitatively evaluate the emission reduction effect， and accurately identify the main carbon emission sources， of which the carbon emission of the sewage treatment system accounts for about 66% to 75%， and the energy consumption of the aeration link can be reduced by 28% through optimal control. For the first time， this study combined the IPCC carbon emission accounting standard with digital technology to build a digital carbon management platform， which realized the comprehensive monitoring， accurate accounting， and optimal regulation of carbon emissions. It provides quantifiable management tools and practical references for pollution reduction and carbon reduction in sewage treatment plants， and has important demonstration significance for promoting the green and low-carbon transformation of the whole industry.
