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2025, Volume 43, Issue 8
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2025,
43(8):
1-13.
doi: 10.13205/j.hjgc.202508099
Abstract:
Municipal wastewater treatment plants are undergoing a profound paradigm shift， transitioning from their traditional role of pollutant removal to becoming integrated hubs for resource and energy recovery. Through a systematic review of the development of wastewater treatment technology， the complete evolution is clearly demonstrated—from initial water body self-purification， to organic matter removal， then to nitrogen and phosphorus elimination， and finally to contemporary resource recycling. This development reflects not only advances in wastewater treatment technology， but also a deepening human understanding of environmental protection and resource recycling. During in-depth analysis of current treatment methods， it is not difficult to identify their existing limitations. For example， carbon emissions are more significant in the current treatment process， including direct emissions of methane （CH4） and nitrous oxide （N2O）， as well as indirect emissions from high power consumption. There are also issues of secondary pollution across the media， such as fugitive odor gases and difficulties in sludge disposal. These issues not only affect the environmental benefits of wastewater treatment plants， but also harm their economic benefits and social image. For the three main pollutants， namely carbon， nitrogen， and phosphorus， the treatment technologies commonly used in wastewater treatment plants at present include anaerobic digestion， bioelectrochemical systems， and chemical precipitation， which are effective in recovering resource products， such as methane， ammonium salts， and guano stones. In the future， wastewater treatment plants will need to adopt the core concept of "targeted conversion and resourcing" and actively seek breakthroughs in emerging technologies to achieve precise， targeted conversion and resource recovery from pollutants such as carbon， nitrogen， phosphorus， and sulfur. To achieve this goal， these plants should build a system characterized by "resource demand-led， technology innovation-driven， and directed conversion implementation". Through this system， it can promote the formation of a "water-energy-material" composite system and realize the transformation from an end-to-end treatment unit to a multi-resource symbiosis hub. Such a transformation will not only help to enhance the comprehensive benefits of the wastewater treatment plant but also provide sustainable solutions for achieving the Double Carbon Goals and promoting the circular economy， thus making greater contributions to the sustainable development of society.
Municipal wastewater treatment plants are undergoing a profound paradigm shift， transitioning from their traditional role of pollutant removal to becoming integrated hubs for resource and energy recovery. Through a systematic review of the development of wastewater treatment technology， the complete evolution is clearly demonstrated—from initial water body self-purification， to organic matter removal， then to nitrogen and phosphorus elimination， and finally to contemporary resource recycling. This development reflects not only advances in wastewater treatment technology， but also a deepening human understanding of environmental protection and resource recycling. During in-depth analysis of current treatment methods， it is not difficult to identify their existing limitations. For example， carbon emissions are more significant in the current treatment process， including direct emissions of methane （CH4） and nitrous oxide （N2O）， as well as indirect emissions from high power consumption. There are also issues of secondary pollution across the media， such as fugitive odor gases and difficulties in sludge disposal. These issues not only affect the environmental benefits of wastewater treatment plants， but also harm their economic benefits and social image. For the three main pollutants， namely carbon， nitrogen， and phosphorus， the treatment technologies commonly used in wastewater treatment plants at present include anaerobic digestion， bioelectrochemical systems， and chemical precipitation， which are effective in recovering resource products， such as methane， ammonium salts， and guano stones. In the future， wastewater treatment plants will need to adopt the core concept of "targeted conversion and resourcing" and actively seek breakthroughs in emerging technologies to achieve precise， targeted conversion and resource recovery from pollutants such as carbon， nitrogen， phosphorus， and sulfur. To achieve this goal， these plants should build a system characterized by "resource demand-led， technology innovation-driven， and directed conversion implementation". Through this system， it can promote the formation of a "water-energy-material" composite system and realize the transformation from an end-to-end treatment unit to a multi-resource symbiosis hub. Such a transformation will not only help to enhance the comprehensive benefits of the wastewater treatment plant but also provide sustainable solutions for achieving the Double Carbon Goals and promoting the circular economy， thus making greater contributions to the sustainable development of society.
2025,
43(8):
14-27.
doi: 10.13205/j.hjgc.202508001
Abstract:
Heterogeneous Fenton oxidation， a representative advanced oxidation technology， primarily degrades organic pollutants by activating H2O2 to generate highly oxidative reactive oxygen species， such as hydroxyl radicals. This method has been widely applied in the advanced treatment of industrial and domestic wastewater. Iron-based heterogeneous Fenton catalysts have gathered significant research interest， due to their eco-friendliness and low cost. However， challenges such as low catalytic efficiency and a narrow applicable pH range remain. In recent years， substantial efforts have been made to broaden the pH range and enhance the catalytic activity of iron-based heterogeneous Fenton catalysts， effectively addressing some limitations of the Fenton oxidation process. This review systematically introduces the fundamental principles of heterogeneous Fenton oxidation and the mechanisms for extending its pH applicability. It also summarizes recent advances， both domestically and internationally， in strategies such as the introduction of external physical fields， chemical auxiliary agents， and internal structural regulation of catalysts to broaden the pH range. Additionally， it highlights critical scientific issues that need to address and discusses future research directions， providing valuable insights for developing high-activity iron-based heterogeneous Fenton catalysts with a wide pH applicability.
Heterogeneous Fenton oxidation， a representative advanced oxidation technology， primarily degrades organic pollutants by activating H2O2 to generate highly oxidative reactive oxygen species， such as hydroxyl radicals. This method has been widely applied in the advanced treatment of industrial and domestic wastewater. Iron-based heterogeneous Fenton catalysts have gathered significant research interest， due to their eco-friendliness and low cost. However， challenges such as low catalytic efficiency and a narrow applicable pH range remain. In recent years， substantial efforts have been made to broaden the pH range and enhance the catalytic activity of iron-based heterogeneous Fenton catalysts， effectively addressing some limitations of the Fenton oxidation process. This review systematically introduces the fundamental principles of heterogeneous Fenton oxidation and the mechanisms for extending its pH applicability. It also summarizes recent advances， both domestically and internationally， in strategies such as the introduction of external physical fields， chemical auxiliary agents， and internal structural regulation of catalysts to broaden the pH range. Additionally， it highlights critical scientific issues that need to address and discusses future research directions， providing valuable insights for developing high-activity iron-based heterogeneous Fenton catalysts with a wide pH applicability.
2025,
43(8):
28-39.
doi: 10.13205/j.hjgc.202508002
Abstract:
This study focused on the secondary effluent of a municipal wastewater treatment plant in Beijing， investigated the removal efficiency of antibiotic resistance genes （ARGs） using an advanced oxidation process （AOP） combining ultraviolet （UV） light and sodium chlorite （NaClO2）. The variations in absolute abundance of nine typical ARGs （tetA，aadA，lnuB，blaTEM，ermF，qnrS，intI1，sul1，sul2） were analyzed using real-time quantitative PCR （qPCR）， revealing the impact of water environmental parameters on ARGs removal in the UV/NaClO2 process. The results indicated that the UV/NaClO2 process significantly outperformed individual UV disinfection or NaClO2 disinfection in removing ARGs from secondary effluent. With UV disinfection alone， the maximum removal efficiency for 16S rRNA was 74.2%， while with NaClO2 disinfection alone， the removal efficiency for 16S rRNA reached a maximum of 75.6%， when the NaClO2 dosage was 0.9 g/L. Under the optimal treatment conditions of UV/NaClO2 （UV254/0.54 g/L NaClO2）， the removal efficiency for 16S rRNA reached 99.5%， and the overall ARGs removal efficiency reached 99.9%. Notably， the removal of sulfonamide-resistant genes， sul1 and sul2， was particularly effective. The optimal disinfection time for the UV/NaClO2 advanced oxidation process was 30 minutes， with removal efficiencies for genes such as ermF， aadA， and tetA exceeding 99.9%. Water environmental factors significantly impacted the removal efficiency of the UV/NaClO2 process. The results showed that neutral conditions（pH=7） were more favorable for ARGs removal. The presence of different concentrations of Cl-， HCO3-， and natural organic matter （NOM） in the UV/NaClO2 system inhibited the removal of ARGs. This study provides theoretical support for the application of the UV/NaClO2 advanced oxidation process in wastewater effluent disinfection， as well as scientific guidance for optimizing process parameters and improving ARGs removal efficiency.
This study focused on the secondary effluent of a municipal wastewater treatment plant in Beijing， investigated the removal efficiency of antibiotic resistance genes （ARGs） using an advanced oxidation process （AOP） combining ultraviolet （UV） light and sodium chlorite （NaClO2）. The variations in absolute abundance of nine typical ARGs （tetA，aadA，lnuB，blaTEM，ermF，qnrS，intI1，sul1，sul2） were analyzed using real-time quantitative PCR （qPCR）， revealing the impact of water environmental parameters on ARGs removal in the UV/NaClO2 process. The results indicated that the UV/NaClO2 process significantly outperformed individual UV disinfection or NaClO2 disinfection in removing ARGs from secondary effluent. With UV disinfection alone， the maximum removal efficiency for 16S rRNA was 74.2%， while with NaClO2 disinfection alone， the removal efficiency for 16S rRNA reached a maximum of 75.6%， when the NaClO2 dosage was 0.9 g/L. Under the optimal treatment conditions of UV/NaClO2 （UV254/0.54 g/L NaClO2）， the removal efficiency for 16S rRNA reached 99.5%， and the overall ARGs removal efficiency reached 99.9%. Notably， the removal of sulfonamide-resistant genes， sul1 and sul2， was particularly effective. The optimal disinfection time for the UV/NaClO2 advanced oxidation process was 30 minutes， with removal efficiencies for genes such as ermF， aadA， and tetA exceeding 99.9%. Water environmental factors significantly impacted the removal efficiency of the UV/NaClO2 process. The results showed that neutral conditions（pH=7） were more favorable for ARGs removal. The presence of different concentrations of Cl-， HCO3-， and natural organic matter （NOM） in the UV/NaClO2 system inhibited the removal of ARGs. This study provides theoretical support for the application of the UV/NaClO2 advanced oxidation process in wastewater effluent disinfection， as well as scientific guidance for optimizing process parameters and improving ARGs removal efficiency.
2025,
43(8):
40-48.
doi: 10.13205/j.hjgc.202508003
Abstract:
To assess the pollution characteristics of pharmaceuticals and personal care products （PPCPs） and their ecological effects in the highly urbanized rivers in Shenzhen， this study selected four typical urban rivers in the Shenzhen River Basin， namely the Shenzhen River， Buji River， Futian River， and Dasha River. The spatial and temporal distribution of 16 PPCPs was detected， the community structure of planktonic bacteria was analyzed， and the driving effects of PPCPs （on bacterial community） was examined. A total of 15 PPCPs were detected in the four rivers of Shenzhen， with a total average concentration of 1127.75 ng/L， range from 148.74 to 2427.4 ng/L. The concentration in the wet season （1518.27 ng/L） was significantly higher than that in the dry season （737.23 ng/L）， among which lincomycin and caffeine were the main pollutants. Among the four rivers， the Dasha River had relatively lower PPCPs concentrations. The planktonic bacterial community in the rivers of Shenzhen was dominated by Proteobacteria （average relative abundance： 38.7%） and Bacteroidetes （21.3%）. The Shenzhen River exhibited the highest species richness， and there were significant differences in planktonic bacterial community compositions among different rivers， whereas no significant differences were observed in the planktonic bacterial community compositions between the wet season and dry season for the same river. Redundancy analysis revealed that in the wet season， roxithromycin， clarithromycin， ciprofloxacin， atenolol， and caffeine significantly drove the structure of river bacterial communities； in the dry season， however， caffeine and atenolol were the main driving factors. These results indicate that PPCPs pollution in the Shenzhen River Basin potentially affects the structure of river microbial communities and their ecological functions. Therefore， dynamic monitoring and risk management of emerging pollutants in the basin should be strengthened.
To assess the pollution characteristics of pharmaceuticals and personal care products （PPCPs） and their ecological effects in the highly urbanized rivers in Shenzhen， this study selected four typical urban rivers in the Shenzhen River Basin， namely the Shenzhen River， Buji River， Futian River， and Dasha River. The spatial and temporal distribution of 16 PPCPs was detected， the community structure of planktonic bacteria was analyzed， and the driving effects of PPCPs （on bacterial community） was examined. A total of 15 PPCPs were detected in the four rivers of Shenzhen， with a total average concentration of 1127.75 ng/L， range from 148.74 to 2427.4 ng/L. The concentration in the wet season （1518.27 ng/L） was significantly higher than that in the dry season （737.23 ng/L）， among which lincomycin and caffeine were the main pollutants. Among the four rivers， the Dasha River had relatively lower PPCPs concentrations. The planktonic bacterial community in the rivers of Shenzhen was dominated by Proteobacteria （average relative abundance： 38.7%） and Bacteroidetes （21.3%）. The Shenzhen River exhibited the highest species richness， and there were significant differences in planktonic bacterial community compositions among different rivers， whereas no significant differences were observed in the planktonic bacterial community compositions between the wet season and dry season for the same river. Redundancy analysis revealed that in the wet season， roxithromycin， clarithromycin， ciprofloxacin， atenolol， and caffeine significantly drove the structure of river bacterial communities； in the dry season， however， caffeine and atenolol were the main driving factors. These results indicate that PPCPs pollution in the Shenzhen River Basin potentially affects the structure of river microbial communities and their ecological functions. Therefore， dynamic monitoring and risk management of emerging pollutants in the basin should be strengthened.
2025,
43(8):
49-59.
doi: 10.13205/j.hjgc.202508004
Abstract:
Perfluoroalkyl substances （PFASs） are widely used in industrial fields and daily life due to their high chemical stability. They exhibit high biological toxicity and persistence and have become a typical persistent organic pollutant with global concern. The highly stable carbon-fluorine bonds in PFASs make them difficult to degrade completely by conventional methods. Pyrolysis technology has become an important approach for degrading PFASs due to its efficient cracking of C—F and C—C bonds and low operating cost. This review summarizes the effects of PFASs’ intrinsic physical and chemical properties （functional groups and chain lengths） and reaction conditions （temperature， atmosphere， and catalysts） on their pyrolysis. It was found that the lower the thermal stability of the functional group itself， the lower the pyrolysis temperature of PFASs； similarly， longer chain lengths also resulted in lower pyrolysis temperatures. As the temperature increased， the degradation efficiency and defluorination rate of PFASs improved， and the required pyrolysis time decreased. Oxygen and water vapor were observed to accelerate the oxidative decomposition of PFASs. Activated carbon and aluminum/copper oxides reduced the pyrolysis temperature of PFASs through adsorption， thereby promoting low-temperature decomposition. Calcium/sodium-based catalysts improved the defluorination efficiency of PFASs and reduce the formation of volatile organic fluorine products. Furthermore， this review outlines the reaction pathways of PFASs pyrolysis， which mainly include three stages： removal of head functional groups， carbon chain scission， and the formation of short-chain perfluorocarbons and inorganic fluorides. Finally， this paper emphasizes that achieving complete and harmless treatment of PFASs requires further in-depth exploration of the molecular dynamics mechanisms underlying PFASs degradation， optimization of catalysts， and control of the formation of short-chain perfluorocarbons. This review aims to provide a theoretical basis and technical support for the treatment of PFASs-containing solid waste， ultimately achieving the goal of harmless treatment of PFASs.
Perfluoroalkyl substances （PFASs） are widely used in industrial fields and daily life due to their high chemical stability. They exhibit high biological toxicity and persistence and have become a typical persistent organic pollutant with global concern. The highly stable carbon-fluorine bonds in PFASs make them difficult to degrade completely by conventional methods. Pyrolysis technology has become an important approach for degrading PFASs due to its efficient cracking of C—F and C—C bonds and low operating cost. This review summarizes the effects of PFASs’ intrinsic physical and chemical properties （functional groups and chain lengths） and reaction conditions （temperature， atmosphere， and catalysts） on their pyrolysis. It was found that the lower the thermal stability of the functional group itself， the lower the pyrolysis temperature of PFASs； similarly， longer chain lengths also resulted in lower pyrolysis temperatures. As the temperature increased， the degradation efficiency and defluorination rate of PFASs improved， and the required pyrolysis time decreased. Oxygen and water vapor were observed to accelerate the oxidative decomposition of PFASs. Activated carbon and aluminum/copper oxides reduced the pyrolysis temperature of PFASs through adsorption， thereby promoting low-temperature decomposition. Calcium/sodium-based catalysts improved the defluorination efficiency of PFASs and reduce the formation of volatile organic fluorine products. Furthermore， this review outlines the reaction pathways of PFASs pyrolysis， which mainly include three stages： removal of head functional groups， carbon chain scission， and the formation of short-chain perfluorocarbons and inorganic fluorides. Finally， this paper emphasizes that achieving complete and harmless treatment of PFASs requires further in-depth exploration of the molecular dynamics mechanisms underlying PFASs degradation， optimization of catalysts， and control of the formation of short-chain perfluorocarbons. This review aims to provide a theoretical basis and technical support for the treatment of PFASs-containing solid waste， ultimately achieving the goal of harmless treatment of PFASs.
2025,
43(8):
60-71.
doi: 10.13205/j.hjgc.202508005
Abstract:
Hydrological processes are the fundamental driving force of wetland vegetation community succession. Poyang Lake， the largest freshwater wetland in China， exhibits highly dynamic vegetation structures and spatial distributions that are highly sensitive to variations in hydrological conditions. Since 2003， changes in the relationship between the Yangtze River and Poyang Lake have led to an earlier onset and prolonged duration of low-water periods， accompanied by decreasing average water levels during these periods. These hydrological changes have driven the succession of wetland vegetation communities toward mesophytic and drought-tolerant species， with their distribution elevations shifting progressively downward and aquatic vegetation experiencing significant degradation. This study focuses on the Nanji Wetland National Nature Reserve （NWNNR）， a representative wetland located in the southern part of Poyang Lake. NWNNR is a typical inland estuarine delta with rich and diverse wetland vegetation that supports a large number of wintering waterbirds. The reserve is close to a waterway connected to the Yangtze River. In recent years， changes in the hydrological relationship between the Yangtze River and Poyang Lake have worsened dry-season conditions， and the vegetation community structure and spatial distribution have also changed. However， existing research is still limited. Using field survey data on hydrology， topography， and vegetation， along with historical datasets， a population dynamics model driven by hydrological processes was developed by coupling the Lotka-Volterra model and the cellular automata model. This model can capture the spatiotemporal dynamics of vegetation expansion under varying hydrological conditions. The simulation results revealed significant differences in the response of wetland vegetation area to different hydrological cycles. During high-water periods， the wetland vegetation area contracted noticeably， whereas during normal- and low- water periods， vegetation expansion was prominent， with overall stability achieved within 3 to 5 years. The spatial heterogeneity of vegetation expansion was also evident， with weaker expansion observed in the delta of the northern branch of the Ganjiang River compared to the middle and southern branches. Longer inundation durations and higher inflow volumes in the northern branch were identified as key factors driving this disparity. Among the four dominant vegetation species， Carex spp. and Phalaris arundinacea exhibited relatively large expansion， predominantly on sandbars at elevations of 10 to 13 meters. In contrast， Phragmites australis and Triarrhena lutarioriparia tended to expand along inflow channels at elevations of approximately 14 meters， forming narrow strips no wider than 5 meters. These distribution patterns highlighted the influence of hydrological conditions and species-specific adaptation strategies， such as morphological adjustments or seasonal dormancy， to flooding stress. The results emphasize that hydrological processes not only govern vegetation dynamics but also influence the overall ecological structure and function of the wetland. This study provides critical insights into the mechanisms driving wetland vegetation dynamics under varying hydrological regimes. Furthermore， it underscores the importance of integrating hydrological management with ecological restoration to enhance habitat quality and biodiversity. The developed model serves as a valuable tool for predicting the impacts of future hydrological changes on wetland vegetation and offers guidance for sustainable wetland management. By improving the understanding of the interactions between hydrological processes and wetland vegetation， this research contributes to the development of effective conservation strategies and the maintenance of ecosystem services in dynamic freshwater wetlands.
Hydrological processes are the fundamental driving force of wetland vegetation community succession. Poyang Lake， the largest freshwater wetland in China， exhibits highly dynamic vegetation structures and spatial distributions that are highly sensitive to variations in hydrological conditions. Since 2003， changes in the relationship between the Yangtze River and Poyang Lake have led to an earlier onset and prolonged duration of low-water periods， accompanied by decreasing average water levels during these periods. These hydrological changes have driven the succession of wetland vegetation communities toward mesophytic and drought-tolerant species， with their distribution elevations shifting progressively downward and aquatic vegetation experiencing significant degradation. This study focuses on the Nanji Wetland National Nature Reserve （NWNNR）， a representative wetland located in the southern part of Poyang Lake. NWNNR is a typical inland estuarine delta with rich and diverse wetland vegetation that supports a large number of wintering waterbirds. The reserve is close to a waterway connected to the Yangtze River. In recent years， changes in the hydrological relationship between the Yangtze River and Poyang Lake have worsened dry-season conditions， and the vegetation community structure and spatial distribution have also changed. However， existing research is still limited. Using field survey data on hydrology， topography， and vegetation， along with historical datasets， a population dynamics model driven by hydrological processes was developed by coupling the Lotka-Volterra model and the cellular automata model. This model can capture the spatiotemporal dynamics of vegetation expansion under varying hydrological conditions. The simulation results revealed significant differences in the response of wetland vegetation area to different hydrological cycles. During high-water periods， the wetland vegetation area contracted noticeably， whereas during normal- and low- water periods， vegetation expansion was prominent， with overall stability achieved within 3 to 5 years. The spatial heterogeneity of vegetation expansion was also evident， with weaker expansion observed in the delta of the northern branch of the Ganjiang River compared to the middle and southern branches. Longer inundation durations and higher inflow volumes in the northern branch were identified as key factors driving this disparity. Among the four dominant vegetation species， Carex spp. and Phalaris arundinacea exhibited relatively large expansion， predominantly on sandbars at elevations of 10 to 13 meters. In contrast， Phragmites australis and Triarrhena lutarioriparia tended to expand along inflow channels at elevations of approximately 14 meters， forming narrow strips no wider than 5 meters. These distribution patterns highlighted the influence of hydrological conditions and species-specific adaptation strategies， such as morphological adjustments or seasonal dormancy， to flooding stress. The results emphasize that hydrological processes not only govern vegetation dynamics but also influence the overall ecological structure and function of the wetland. This study provides critical insights into the mechanisms driving wetland vegetation dynamics under varying hydrological regimes. Furthermore， it underscores the importance of integrating hydrological management with ecological restoration to enhance habitat quality and biodiversity. The developed model serves as a valuable tool for predicting the impacts of future hydrological changes on wetland vegetation and offers guidance for sustainable wetland management. By improving the understanding of the interactions between hydrological processes and wetland vegetation， this research contributes to the development of effective conservation strategies and the maintenance of ecosystem services in dynamic freshwater wetlands.
2025,
43(8):
72-85.
doi: 10.13205/j.hjgc.202508006
Abstract:
Volatile organic compounds （VOCs） and nitrogen oxides （NO x ） are the key precursors of fine particulate matter （PM2.5） and ozone in the atmosphere， posing a serious threat to both the ecological environment and human health. In order to solve the problem of multi-pollutant control， catalytic technology has been widely used for the synergistic removal of VOCs and NO x， showing high efficiency and environmental friendliness. This paper provides a systematic review of catalytic technologies for the synergistic removal of VOCs and NO x . By comparing the reaction mechanisms of individual versus synergistic removal， it demonstrates that the compositional design of catalysts，particularly the modulation of active components （e.g.， transition metals and noble metals） and support materials （such as MOF and graphene），can significantly enhance catalytic efficiency and stability. Through structural optimization and rational design strategies， including precise construction of atomic-scale catalytic sites， nano-scale interface regulation， and intelligent response mechanisms， catalyst performance can be effectively improved. Furthermore， significant progress has been made in novel catalytic approaches such as photocatalysis， photoelectrocatalysis， and photothermal catalysis. This study elucidates key mechanisms and technological advances in synergistic removal， proposes future development directions for catalysts and catalytic methods， and provides theoretical guidance for the efficient synergistic control of VOCs and NO x.
Volatile organic compounds （VOCs） and nitrogen oxides （NO x ） are the key precursors of fine particulate matter （PM2.5） and ozone in the atmosphere， posing a serious threat to both the ecological environment and human health. In order to solve the problem of multi-pollutant control， catalytic technology has been widely used for the synergistic removal of VOCs and NO x， showing high efficiency and environmental friendliness. This paper provides a systematic review of catalytic technologies for the synergistic removal of VOCs and NO x . By comparing the reaction mechanisms of individual versus synergistic removal， it demonstrates that the compositional design of catalysts，particularly the modulation of active components （e.g.， transition metals and noble metals） and support materials （such as MOF and graphene），can significantly enhance catalytic efficiency and stability. Through structural optimization and rational design strategies， including precise construction of atomic-scale catalytic sites， nano-scale interface regulation， and intelligent response mechanisms， catalyst performance can be effectively improved. Furthermore， significant progress has been made in novel catalytic approaches such as photocatalysis， photoelectrocatalysis， and photothermal catalysis. This study elucidates key mechanisms and technological advances in synergistic removal， proposes future development directions for catalysts and catalytic methods， and provides theoretical guidance for the efficient synergistic control of VOCs and NO x.
2025,
43(8):
86-95.
doi: 10.13205/j.hjgc.202508007
Abstract:
The ecological environment and health effects of atmospheric components have received widespread attention. Previous studies focused primarily on the adverse effects of atmospheric pollutants， while less attention was paid to the beneficial components that contribute to ecological regulation and human well-being. This review systematically summarized the primary sources， environmental and health effects of chemical and biological beneficial components in the atmosphere， especially the key representatives of negative air ions， biogenic volatile organic compounds， and atmospheric microorganisms. Key influencing factors on the concentration and composition of beneficial atmospheric components in natural and urban environments were examined， and the reactivity and atmospheric evolution of these components under different conditions were discussed. This review provides an outlook on future research directions in the field of beneficial atmospheric components and aims to offer theoretical guidance for urban planning， ecological landscape design， and public health management， thereby enhancing urban ecosystem services and improving public health and well-being.
The ecological environment and health effects of atmospheric components have received widespread attention. Previous studies focused primarily on the adverse effects of atmospheric pollutants， while less attention was paid to the beneficial components that contribute to ecological regulation and human well-being. This review systematically summarized the primary sources， environmental and health effects of chemical and biological beneficial components in the atmosphere， especially the key representatives of negative air ions， biogenic volatile organic compounds， and atmospheric microorganisms. Key influencing factors on the concentration and composition of beneficial atmospheric components in natural and urban environments were examined， and the reactivity and atmospheric evolution of these components under different conditions were discussed. This review provides an outlook on future research directions in the field of beneficial atmospheric components and aims to offer theoretical guidance for urban planning， ecological landscape design， and public health management， thereby enhancing urban ecosystem services and improving public health and well-being.
2025,
43(8):
96-106.
doi: 10.13205/j.hjgc.202508008
Abstract:
Volatile organic compounds （VOCs） are important precursors of secondary pollution such as tropospheric O3 and secondary organic aerosol （SOA）. Their photochemical reactions dominate the formation of photochemical smog， playing a critical role in the generation of urban and regional O3. The analysis of VOCs concentrations and their environmental effects in industrial parks is of great significance to carry out pollution remediation actions and control the generation of O3 in this area. To study the pollution characteristics and sources in the Kaifeng's Fine Chemical Industrial Park， based on the results of tank sampling analysis， species correlation analysis was used， and a positive matrix factorization （PMF） model was applied to conduct source apportionment research. The findings revealed that the average concentration of VOCs in the fine chemical industrial park was 410.65μg/m3， with halogenated hydrocarbons accounting for 49%， aromatic hydrocarbons for 20.4%， oxygen-containing volatile organic compounds （OVOCs） for 16.4%， alkanes for 12.2%， alkenes for 1.7%， and carbon disulfide （CS2） for 0.2%. The ozone formation potential （OFP） was measured at 3066.47 µg/m3， with aromatic hydrocarbons contributing the most （53.1%）， followed by OVOCs （29.1%）， alkenes （8.1%）， alkanes （5.7%）， and halogenated hydrocarbons （4%）. Toluene， ethylbenzene， m-p-xylene， and o-xylene were identified as the dominant species among aromatic hydrocarbons， while isoprene played a significant role in olefin with a contribution rate of 2.7% to OFP. The SOA concentration was determined to be 18.98 µg/m3， with aromatic hydrocarbons and long-chain alkanes contributing approximately 89.83% and 9.33%， respectively. C7（23.2%） and C8（46.5%） were identified as the main contributors to SOA formation among aromatic hydrocarbons. The correlation analysis indicated that industrial activities， solvent use， and vehicle emissions significantly influenced the air quality. Furthermore， PMF model analysis identified five major contributors to VOCs concentration in this fine chemical industrial park： combustion sources （5.6%）， industrial activities （including solvent/paint use， 22.6%）， regional aging air masses （27.4%）， biogenic sources （14%）， and motor vehicle exhaust emissions （30%）.
Volatile organic compounds （VOCs） are important precursors of secondary pollution such as tropospheric O3 and secondary organic aerosol （SOA）. Their photochemical reactions dominate the formation of photochemical smog， playing a critical role in the generation of urban and regional O3. The analysis of VOCs concentrations and their environmental effects in industrial parks is of great significance to carry out pollution remediation actions and control the generation of O3 in this area. To study the pollution characteristics and sources in the Kaifeng's Fine Chemical Industrial Park， based on the results of tank sampling analysis， species correlation analysis was used， and a positive matrix factorization （PMF） model was applied to conduct source apportionment research. The findings revealed that the average concentration of VOCs in the fine chemical industrial park was 410.65μg/m3， with halogenated hydrocarbons accounting for 49%， aromatic hydrocarbons for 20.4%， oxygen-containing volatile organic compounds （OVOCs） for 16.4%， alkanes for 12.2%， alkenes for 1.7%， and carbon disulfide （CS2） for 0.2%. The ozone formation potential （OFP） was measured at 3066.47 µg/m3， with aromatic hydrocarbons contributing the most （53.1%）， followed by OVOCs （29.1%）， alkenes （8.1%）， alkanes （5.7%）， and halogenated hydrocarbons （4%）. Toluene， ethylbenzene， m-p-xylene， and o-xylene were identified as the dominant species among aromatic hydrocarbons， while isoprene played a significant role in olefin with a contribution rate of 2.7% to OFP. The SOA concentration was determined to be 18.98 µg/m3， with aromatic hydrocarbons and long-chain alkanes contributing approximately 89.83% and 9.33%， respectively. C7（23.2%） and C8（46.5%） were identified as the main contributors to SOA formation among aromatic hydrocarbons. The correlation analysis indicated that industrial activities， solvent use， and vehicle emissions significantly influenced the air quality. Furthermore， PMF model analysis identified five major contributors to VOCs concentration in this fine chemical industrial park： combustion sources （5.6%）， industrial activities （including solvent/paint use， 22.6%）， regional aging air masses （27.4%）， biogenic sources （14%）， and motor vehicle exhaust emissions （30%）.
2025,
43(8):
107-116.
doi: 10.13205/j.hjgc.202508009
Abstract:
Practical engineering applications and previous studies have confirmed that perforated electrostatic precipitator （ESP） plates are effective at suppressing dust re-entrainment. However， the current design of their structural parameters still mainly relies on empirical experience. Moreover， the effects of perforated plate structures on the electric field properties of electrostatic precipitators， the suppression of dust re-entrainment， and the improvement of dust collection efficiency are still limited to qualitative analysis and lack robust quantitative theoretical support. Using numerical methods， this study investigated the effects of perforated plate porosity and baffle configurations—including the relative position between the baffle and the perforated plate， baffle inclination angle， baffle spacing， and the clearance between the baffle and the plate—on the electric field， flow field， and dust removal efficiency of the perforated-plate electrostatic precipitator. The results showed that the presence of baffles had a positive effect on the perforated-plate electrostatic precipitator. Based on the goals of suppressing dust re-entrainment and improving collection efficiency， the optimal design parameters were determined as follows： a porosity of 43.5%， a baffle orientation facing the center of the plate， a baffle inclination angle of 55°， a baffle-to-plate clearance of 3 mm， and a baffle spacing of 45 mm.
Practical engineering applications and previous studies have confirmed that perforated electrostatic precipitator （ESP） plates are effective at suppressing dust re-entrainment. However， the current design of their structural parameters still mainly relies on empirical experience. Moreover， the effects of perforated plate structures on the electric field properties of electrostatic precipitators， the suppression of dust re-entrainment， and the improvement of dust collection efficiency are still limited to qualitative analysis and lack robust quantitative theoretical support. Using numerical methods， this study investigated the effects of perforated plate porosity and baffle configurations—including the relative position between the baffle and the perforated plate， baffle inclination angle， baffle spacing， and the clearance between the baffle and the plate—on the electric field， flow field， and dust removal efficiency of the perforated-plate electrostatic precipitator. The results showed that the presence of baffles had a positive effect on the perforated-plate electrostatic precipitator. Based on the goals of suppressing dust re-entrainment and improving collection efficiency， the optimal design parameters were determined as follows： a porosity of 43.5%， a baffle orientation facing the center of the plate， a baffle inclination angle of 55°， a baffle-to-plate clearance of 3 mm， and a baffle spacing of 45 mm.
2025,
43(8):
117-128.
doi: 10.13205/j.hjgc.202508010
Abstract:
From March 2022 to February 2023， the light absorption coefficients of black carbon （BC） in aerosols were measured using an Aethalometer （AE33） at a northern suburban site of Nanjing. In combination with backward trajectory analysis， potential source contributions， and diurnal variation patterns， the seasonal characteristics of BC were systematically investigated. The results showed that the annual mean BC absorption coefficient at 880 nm was 13.23 Mm-1， with significant seasonal variations （highest in winter and lowest in summer） and a distinct diurnal pattern （bimodal distribution， aligning with traffic peaks）. At 370 nm， the absorption contribution of brown carbon （BrC） increased significantly in winter （accounting for 38.4%）， while it was lowest in summer （21.4%）. The annual mean aerosol absorption Ångström exponent （AAE） was 1.37 ± 0.19， peaking in winter （1.51 ± 0.20）， indicating increased contributions from biomass burning. Source apportionment analysis revealed that fossil fuel combustion was the primary BC source （68.7%）， but biomass burning contributed significantly in winter （39.9%）. Meteorological analysis suggested that the low temperature， shallow boundary layer， and weak wind facilitated BC accumulation， while relative humidity had a minor influence. Backward trajectory and potential source analyses indicated that BC in summer was influenced by local and regional mixed sources， whereas in spring， autumn， and winter， it was affected by regional transport from surrounding areas. This study provides useful scientific insights for BC pollution control in the Nanjing.
From March 2022 to February 2023， the light absorption coefficients of black carbon （BC） in aerosols were measured using an Aethalometer （AE33） at a northern suburban site of Nanjing. In combination with backward trajectory analysis， potential source contributions， and diurnal variation patterns， the seasonal characteristics of BC were systematically investigated. The results showed that the annual mean BC absorption coefficient at 880 nm was 13.23 Mm-1， with significant seasonal variations （highest in winter and lowest in summer） and a distinct diurnal pattern （bimodal distribution， aligning with traffic peaks）. At 370 nm， the absorption contribution of brown carbon （BrC） increased significantly in winter （accounting for 38.4%）， while it was lowest in summer （21.4%）. The annual mean aerosol absorption Ångström exponent （AAE） was 1.37 ± 0.19， peaking in winter （1.51 ± 0.20）， indicating increased contributions from biomass burning. Source apportionment analysis revealed that fossil fuel combustion was the primary BC source （68.7%）， but biomass burning contributed significantly in winter （39.9%）. Meteorological analysis suggested that the low temperature， shallow boundary layer， and weak wind facilitated BC accumulation， while relative humidity had a minor influence. Backward trajectory and potential source analyses indicated that BC in summer was influenced by local and regional mixed sources， whereas in spring， autumn， and winter， it was affected by regional transport from surrounding areas. This study provides useful scientific insights for BC pollution control in the Nanjing.
2025,
43(8):
129-136.
doi: 10.13205/j.hjgc.202508011
Abstract:
Microbial fuel cells （MFCs）， as an emerging clean energy technology， are primarily limited by the efficiency of electron transfer at the cathode. Although redox-active substances such as melanin show potential in promoting electron transfer， the effects and mechanisms of melanin from different sources as cathode catalysts on MFC performance remain unclear. This study systematically investigated the catalytic performance of biological and chemical melanin/graphene composites in MFCs. The results showed that biological melanin exhibited superior dispersion stability compared to its chemical counterpart. Although the biological melanin/graphene composite had a relatively lower loading capacity， it demonstrated the highest C-N bond content and beneficial trace element enrichment， particularly Fe， on its surface. Performance evaluation under various carbon sources and temperature conditions revealed that MFCs with biological melanin/graphene composite materials as the cathode catalyst achieved optimal power generation and environmental adaptability. Using sodium acetate as the carbon source， the maximum output voltage reached （0.254±0.003） V， showing increases of 30.3% and 33.7% compared to unmodified graphene and chemical melanin/graphene composite materials， respectively. Mechanistic studies indicated that biological melanin/graphene composite materials significantly enhanced microbial cell viability and improved the relative reducing power and electron transfer levels within mixed microorganisms， leading to substantially improved MFC performance. This research not only provides new insights into developing efficient MFC cathode catalysts but also establishes a theoretical foundation for understanding key factors in electron transfer processes.
Microbial fuel cells （MFCs）， as an emerging clean energy technology， are primarily limited by the efficiency of electron transfer at the cathode. Although redox-active substances such as melanin show potential in promoting electron transfer， the effects and mechanisms of melanin from different sources as cathode catalysts on MFC performance remain unclear. This study systematically investigated the catalytic performance of biological and chemical melanin/graphene composites in MFCs. The results showed that biological melanin exhibited superior dispersion stability compared to its chemical counterpart. Although the biological melanin/graphene composite had a relatively lower loading capacity， it demonstrated the highest C-N bond content and beneficial trace element enrichment， particularly Fe， on its surface. Performance evaluation under various carbon sources and temperature conditions revealed that MFCs with biological melanin/graphene composite materials as the cathode catalyst achieved optimal power generation and environmental adaptability. Using sodium acetate as the carbon source， the maximum output voltage reached （0.254±0.003） V， showing increases of 30.3% and 33.7% compared to unmodified graphene and chemical melanin/graphene composite materials， respectively. Mechanistic studies indicated that biological melanin/graphene composite materials significantly enhanced microbial cell viability and improved the relative reducing power and electron transfer levels within mixed microorganisms， leading to substantially improved MFC performance. This research not only provides new insights into developing efficient MFC cathode catalysts but also establishes a theoretical foundation for understanding key factors in electron transfer processes.
2025,
43(8):
137-147.
doi: 10.13205/j.hjgc.202508012
Abstract:
This research analyzed data on 49 types of hazardous waste across 11 cities in Hebei Province in year 2017 to 2021. An in-depth analysis was conducted from multiple perspectives， including the generation volume， disposal volume， the number of disposal enterprises， and the types of hazardous waste. Furthermore， the hazardous waste generation in year 2022 to 2030 for each city was predicted. The results showed that in 2021， the generation and disposal of hazardous waste in Hebei Province reached 8.8407 million tons and 0.8020 million tons， respectively， with approximately 31 types of hazardous waste identified. The 11 cities in the province， which are at different stages of economic and industrial development， face distinct extent of challenges. Among them， Tangshan generated the largest amount of hazardous waste， reaching 4.0715 million tons. Finally， a GM（1，1） prediction model was established to predict the future generation of hazardous waste. The results indicate that it will grow exponentially in Hebei Province in year 2022 to 2030， and reach 19.0320 million tons by 2030. This research aims to provide data support and decision-making references for addressing current challenges in hazardous waste treatment and disposal， as well as guiding future ecological environment construction in Hebei Province， by conducting a detailed analysis of hazardous waste generation and disposal data and predicting future trends.
This research analyzed data on 49 types of hazardous waste across 11 cities in Hebei Province in year 2017 to 2021. An in-depth analysis was conducted from multiple perspectives， including the generation volume， disposal volume， the number of disposal enterprises， and the types of hazardous waste. Furthermore， the hazardous waste generation in year 2022 to 2030 for each city was predicted. The results showed that in 2021， the generation and disposal of hazardous waste in Hebei Province reached 8.8407 million tons and 0.8020 million tons， respectively， with approximately 31 types of hazardous waste identified. The 11 cities in the province， which are at different stages of economic and industrial development， face distinct extent of challenges. Among them， Tangshan generated the largest amount of hazardous waste， reaching 4.0715 million tons. Finally， a GM（1，1） prediction model was established to predict the future generation of hazardous waste. The results indicate that it will grow exponentially in Hebei Province in year 2022 to 2030， and reach 19.0320 million tons by 2030. This research aims to provide data support and decision-making references for addressing current challenges in hazardous waste treatment and disposal， as well as guiding future ecological environment construction in Hebei Province， by conducting a detailed analysis of hazardous waste generation and disposal data and predicting future trends.
2025,
43(8):
148-157.
doi: 10.13205/j.hjgc.202508013
Abstract:
Improper disposal of crustacean aquatic by-products can easily result in environmental pollution， and the resource utilization can be achieved by extracting chitin， a natural polysaccharide abundant in crustaceans. The main pathways for recovering and extracting chitin from crustacean waste （e.g.， acid-base method， biological method， ionic liquid method， deep eutectic solvent method， and auxiliary technologies such as microwave and ultrasound） and key processes （e.g.， chemical or biological removal of impurities such as minerals and proteins， as well as the dissolution and regeneration of chitin） are reviewed. The effects of key parameters on extraction efficiency （e.g.， purity， demineralization， and deproteinization rates） and chitin characteristics （e.g.， acetylation degree and molecular weight） are analyzed across different methods. Both the advantages and limitations of each method are elaborated in terms of extraction efficacy and environmental friendliness. Finally， the prospects for process optimization and high-value conversion of chitin are discussed， aiming to provide guidance for the resource utilization of crustacean waste.
Improper disposal of crustacean aquatic by-products can easily result in environmental pollution， and the resource utilization can be achieved by extracting chitin， a natural polysaccharide abundant in crustaceans. The main pathways for recovering and extracting chitin from crustacean waste （e.g.， acid-base method， biological method， ionic liquid method， deep eutectic solvent method， and auxiliary technologies such as microwave and ultrasound） and key processes （e.g.， chemical or biological removal of impurities such as minerals and proteins， as well as the dissolution and regeneration of chitin） are reviewed. The effects of key parameters on extraction efficiency （e.g.， purity， demineralization， and deproteinization rates） and chitin characteristics （e.g.， acetylation degree and molecular weight） are analyzed across different methods. Both the advantages and limitations of each method are elaborated in terms of extraction efficacy and environmental friendliness. Finally， the prospects for process optimization and high-value conversion of chitin are discussed， aiming to provide guidance for the resource utilization of crustacean waste.
2025,
43(8):
158-168.
doi: 10.13205/j.hjgc.202508014
Abstract:
Compressed air systems （CAS） are essential infrastructure and crucial power sources in factories， but are also major energy consumers. Selecting optimal equipment combinations and air supply scheduling to match factory’s demand and system’s characteristics is a core energy-saving challenge that requires global optimization. This study proposed a joint scheduling optimization approach that integrating air compressor equipment selection with pipeline network load variations. By incorporating production demand， available compressor configurations， time-of-day tariffs， and uncertainties in air demand fluctuations， we constructed a mixed-integer linear programming （MILP） model aimed at minimizing energy consumption and investment cost of air compressors. Solving the model through global optimization enabled us to achieve optimal equipment combinations and peak-shifting air supply scheduling. By Applying to an industrial park-level CAS， the proposed method produced an overall decision plan including robust optimal equipment combinations and peak-shifting air usage scheduling. Under different air demand scenarios， the system’s peak load can be reduced by 18.05% to 27.16%， energy consumption saved by 33.33% to 34.56%， and annual total cost lowered by 28.35% to 31.42%（ up to 24.88 million yuan）. The results indicated that this optimization strategy， which primarily matches production demand with compressor operation on both the supply and demand sides， effectively balances economic benefits and energy-saving needs. It can provide significant guidance for upgrading and retrofitting CAS.
Compressed air systems （CAS） are essential infrastructure and crucial power sources in factories， but are also major energy consumers. Selecting optimal equipment combinations and air supply scheduling to match factory’s demand and system’s characteristics is a core energy-saving challenge that requires global optimization. This study proposed a joint scheduling optimization approach that integrating air compressor equipment selection with pipeline network load variations. By incorporating production demand， available compressor configurations， time-of-day tariffs， and uncertainties in air demand fluctuations， we constructed a mixed-integer linear programming （MILP） model aimed at minimizing energy consumption and investment cost of air compressors. Solving the model through global optimization enabled us to achieve optimal equipment combinations and peak-shifting air supply scheduling. By Applying to an industrial park-level CAS， the proposed method produced an overall decision plan including robust optimal equipment combinations and peak-shifting air usage scheduling. Under different air demand scenarios， the system’s peak load can be reduced by 18.05% to 27.16%， energy consumption saved by 33.33% to 34.56%， and annual total cost lowered by 28.35% to 31.42%（ up to 24.88 million yuan）. The results indicated that this optimization strategy， which primarily matches production demand with compressor operation on both the supply and demand sides， effectively balances economic benefits and energy-saving needs. It can provide significant guidance for upgrading and retrofitting CAS.
2025,
43(8):
169-176.
doi: 10.13205/j.hjgc.202508015
Abstract:
The rapid expansion of energy infrastructure， particularly oil and gas pipelines， poses escalating risks to ecologically sensitive regions. This study integrates human activity factors into the InVEST habitat risk assessment （HRA） model to evaluate the cumulative habitat risks in the Chaiwopu area，a vulnerable arid ecosystem in Xinjiang， China. By compiling spatial datasets of three stressors （land for urban/village/industrial-mining use， highway land， and oil-gas pipelines） and nine habitat types， we quantified the exposure-consequence relationships and spatial heterogeneity of habitat risks. The results showed that 8.9% of the study area was subjected to medium-to-high habitat risks， which were mainly concentrated in areas of urban/village/industrial-mining land and along the northern shore of Chaiwopu Lake. Secondary habitats， such as low-coverage grasslands （mean risk： 0.29； maximum risk： 3.91） and artificial woodlands， exhibited higher vulnerability due to their low ecosystem complexity. Although oil-gas pipelines alone had a minimal impact， their coupling with the stressor of highway land amplified the maximum risk intensity by 1.32 times and the mean risk by 24.50 times. Further spatial analysis identified synergistic risks at the intersections of pipelines and highways， highlighting the necessity of coordinated land-use planning. The research findings provide a methodological framework for balancing energy development and habitat conservation， and offer references for sustainable management of ecologically sensitive regions.
The rapid expansion of energy infrastructure， particularly oil and gas pipelines， poses escalating risks to ecologically sensitive regions. This study integrates human activity factors into the InVEST habitat risk assessment （HRA） model to evaluate the cumulative habitat risks in the Chaiwopu area，a vulnerable arid ecosystem in Xinjiang， China. By compiling spatial datasets of three stressors （land for urban/village/industrial-mining use， highway land， and oil-gas pipelines） and nine habitat types， we quantified the exposure-consequence relationships and spatial heterogeneity of habitat risks. The results showed that 8.9% of the study area was subjected to medium-to-high habitat risks， which were mainly concentrated in areas of urban/village/industrial-mining land and along the northern shore of Chaiwopu Lake. Secondary habitats， such as low-coverage grasslands （mean risk： 0.29； maximum risk： 3.91） and artificial woodlands， exhibited higher vulnerability due to their low ecosystem complexity. Although oil-gas pipelines alone had a minimal impact， their coupling with the stressor of highway land amplified the maximum risk intensity by 1.32 times and the mean risk by 24.50 times. Further spatial analysis identified synergistic risks at the intersections of pipelines and highways， highlighting the necessity of coordinated land-use planning. The research findings provide a methodological framework for balancing energy development and habitat conservation， and offer references for sustainable management of ecologically sensitive regions.
2025,
43(8):
177-186.
doi: 10.13205/j.hjgc.202508016
Abstract:
Data on the generation and discharge of industrial pollution serves as a critical foundation for industrial pollution prevention and control， environmental management， and scientific research. This study systematically analyzed industrial production and discharge patterns， classified industrial sectors into process-oriented industries （including petroleum refining， chemical production， and steel manufacturing， etc） and discrete industries （including encompassing automotive assembly， electronics manufacturing， and machinery production， etc）. An innovative concept， the minimum pollution-generating benchmark unit （MPGBU）， was proposed. A scientific classification methodology was established by dividing independent units according to process links in process-oriented industries， and by eliminating redundancy through extraction of common characteristic units in discrete industries. By deconstructing each MPGBU， six key influencing factors were identified： raw materials， products， production processes， production scale， types of pollutants， and treatment technologies. Based on these factors， an accounting model for pollutant emissions was developed. To facilitate the application of pollution generation and emission coefficients， a coding system was designed， and a digital management platform was created， enabling the digital management of over 100000 coefficients. In response to emerging needs in precise pollution control， the Dual Carbon Strategy， and the integration of big data and artificial intelligence， further research should be conducted on methods for precise pollution control with MPGBUs as the basic units. We should also explore pollution-carbon synergistic accounting techniques based on MPGBUs， and adopt new information technologies such as big data and artificial intelligence to support new approaches for updating pollution generation and emission coefficients.
Data on the generation and discharge of industrial pollution serves as a critical foundation for industrial pollution prevention and control， environmental management， and scientific research. This study systematically analyzed industrial production and discharge patterns， classified industrial sectors into process-oriented industries （including petroleum refining， chemical production， and steel manufacturing， etc） and discrete industries （including encompassing automotive assembly， electronics manufacturing， and machinery production， etc）. An innovative concept， the minimum pollution-generating benchmark unit （MPGBU）， was proposed. A scientific classification methodology was established by dividing independent units according to process links in process-oriented industries， and by eliminating redundancy through extraction of common characteristic units in discrete industries. By deconstructing each MPGBU， six key influencing factors were identified： raw materials， products， production processes， production scale， types of pollutants， and treatment technologies. Based on these factors， an accounting model for pollutant emissions was developed. To facilitate the application of pollution generation and emission coefficients， a coding system was designed， and a digital management platform was created， enabling the digital management of over 100000 coefficients. In response to emerging needs in precise pollution control， the Dual Carbon Strategy， and the integration of big data and artificial intelligence， further research should be conducted on methods for precise pollution control with MPGBUs as the basic units. We should also explore pollution-carbon synergistic accounting techniques based on MPGBUs， and adopt new information technologies such as big data and artificial intelligence to support new approaches for updating pollution generation and emission coefficients.
2025,
43(8):
187-194.
doi: 10.13205/j.hjgc.202508017
Abstract:
Conventional techniques such as landfill and incineration for sewage sludge treatment carry risks of secondary pollution， making vermicomposting technology an increasingly attractive eco-friendly alternative. This approach leverages the synergistic interaction between earthworms （Eisenia fetida） and microorganisms to significantly shorten composting cycles while enhancing the nutrient content and water retention capacity of the final product. Earthworms improve microbial community structures， accelerate organic matter decomposition， and generate high-value vermicast through feeding activities and mechanical agitation. These advantages demonstrate substantial potential in organic waste degradation and resource recovery. Although the efficacy of vermicomposting in treating textile mill sludge and sugar industry waste has been validated in previous studies， systematic investigations into organic matter transformation mechanisms during vermicomposting for sewage sludge treatment remain inadequate. Five experimental groups with varying sludge-to-cattle manure ratios were established in this study， i.e. V1 （100% sludge）， V2 （70% sludge + 30% manure）， V3 （50% sludge + 50% manure）， V4 （30% sludge + 70% manure）， and V5 （100% manure）. Analytical techniques including organic carbon analysis and infrared spectroscopy were employed to monitor organic matter transformation and humic acid characteristics during vermicomposting. The results showed significant reductions of 27.1%， 31.3%， and 30.9% in volatile suspended solids， total organic carbon， and C/N ratio， respectively， after vermicomposting of sewage sludge. Infrared spectral analysis further revealed structural modifications in humic acid， confirming the effectiveness of earthworm-mediated organic matter transformation. Meanwhile， total bacterial count， respiration rate， and dehydrogenase activity substantially decreased by 70.3%， 89.3%， and 88.0%， respectively， indicating the enhancement of organic matter degradation and microbial stability. Notably， vermicomposting exhibited superior performance in sludge stabilization， biostability， and maturity. The findings imply the feasibility of vermicomposting for sewage sludge treatment， and multi-species comparisons and extended monitoring are required in future study to validate the technology's robustness in practical scenarios.
Conventional techniques such as landfill and incineration for sewage sludge treatment carry risks of secondary pollution， making vermicomposting technology an increasingly attractive eco-friendly alternative. This approach leverages the synergistic interaction between earthworms （Eisenia fetida） and microorganisms to significantly shorten composting cycles while enhancing the nutrient content and water retention capacity of the final product. Earthworms improve microbial community structures， accelerate organic matter decomposition， and generate high-value vermicast through feeding activities and mechanical agitation. These advantages demonstrate substantial potential in organic waste degradation and resource recovery. Although the efficacy of vermicomposting in treating textile mill sludge and sugar industry waste has been validated in previous studies， systematic investigations into organic matter transformation mechanisms during vermicomposting for sewage sludge treatment remain inadequate. Five experimental groups with varying sludge-to-cattle manure ratios were established in this study， i.e. V1 （100% sludge）， V2 （70% sludge + 30% manure）， V3 （50% sludge + 50% manure）， V4 （30% sludge + 70% manure）， and V5 （100% manure）. Analytical techniques including organic carbon analysis and infrared spectroscopy were employed to monitor organic matter transformation and humic acid characteristics during vermicomposting. The results showed significant reductions of 27.1%， 31.3%， and 30.9% in volatile suspended solids， total organic carbon， and C/N ratio， respectively， after vermicomposting of sewage sludge. Infrared spectral analysis further revealed structural modifications in humic acid， confirming the effectiveness of earthworm-mediated organic matter transformation. Meanwhile， total bacterial count， respiration rate， and dehydrogenase activity substantially decreased by 70.3%， 89.3%， and 88.0%， respectively， indicating the enhancement of organic matter degradation and microbial stability. Notably， vermicomposting exhibited superior performance in sludge stabilization， biostability， and maturity. The findings imply the feasibility of vermicomposting for sewage sludge treatment， and multi-species comparisons and extended monitoring are required in future study to validate the technology's robustness in practical scenarios.
2025,
43(8):
195-203.
doi: 10.13205/j.hjgc.202508018
Abstract:
Accurate identification of heavy metal pollution sources is an important prerequisite for soil pollution prevention and control. However， due to the lack of information on the influencing factors of heavy metal pollution， the effectiveness of pollution source tracing analysis is often constrained. In this paper， a typical industrial area was taken as the study area. On the basis of the actual measurements of soil heavy metal concentrations from 577 sampling points and data on the 18 environmental covariates， random forest （RF） and bivariate local spatial autocorrelation methods were applied to identify the influencing factors of the Cd， Pb， and Cr concentrations， determine the quantitative contributions of the 18 influencing factors to these heavy metal concentrations， and further propose targeted strategies for soil environmental management in an industralized study area. The results showed that the optimal performance of the RF prediction models for the three heavy metals was achieved when the coefficient of determination （R2） reached 0.93 with a root mean square error （RMSE） of 0.43 mg/kg for Cd， 0.97 with an RMSE of 48.57 mg/kg for Pb， and 0.93 with an RMSE of 18.57 mg/kg for Cr，namely. There were differences in the spatial distributions of the relative concentrations of the three heavy metals. The regions with relatively high Cd concentration were concentrated in the central and southern parts of the study area， whereas relatively high Pb relative concentration were predominantly found in the central and eastern parts. The regions with relatively high Cr relative concentration were concentrated in the southwestern and northeastern parts. Railway was identified as the most significant factor influencing Cd concentration with a contribution rate of 0.119. Soil pH was identified to be the most significant factor influencing Pb concentrations with a contribution rate of 0.099. The hazardous waste disposal site was identified to be the most significant factor influencing Cr concentrations with a contribution rate of 0.100. Compared to the rest of the study area， the central region exhibited higher concentrations of Cd， Pb， and Cr， more complex human activity， and a greater number of high-high cluster zones， where Cd， Pb， and Cr concentrations and their most significant influencing factors were concentrated. When source prevention and control measures for heavy metal pollution in soil were implemented， particular attention should be prioritized towards the central region of the study area.
Accurate identification of heavy metal pollution sources is an important prerequisite for soil pollution prevention and control. However， due to the lack of information on the influencing factors of heavy metal pollution， the effectiveness of pollution source tracing analysis is often constrained. In this paper， a typical industrial area was taken as the study area. On the basis of the actual measurements of soil heavy metal concentrations from 577 sampling points and data on the 18 environmental covariates， random forest （RF） and bivariate local spatial autocorrelation methods were applied to identify the influencing factors of the Cd， Pb， and Cr concentrations， determine the quantitative contributions of the 18 influencing factors to these heavy metal concentrations， and further propose targeted strategies for soil environmental management in an industralized study area. The results showed that the optimal performance of the RF prediction models for the three heavy metals was achieved when the coefficient of determination （R2） reached 0.93 with a root mean square error （RMSE） of 0.43 mg/kg for Cd， 0.97 with an RMSE of 48.57 mg/kg for Pb， and 0.93 with an RMSE of 18.57 mg/kg for Cr，namely. There were differences in the spatial distributions of the relative concentrations of the three heavy metals. The regions with relatively high Cd concentration were concentrated in the central and southern parts of the study area， whereas relatively high Pb relative concentration were predominantly found in the central and eastern parts. The regions with relatively high Cr relative concentration were concentrated in the southwestern and northeastern parts. Railway was identified as the most significant factor influencing Cd concentration with a contribution rate of 0.119. Soil pH was identified to be the most significant factor influencing Pb concentrations with a contribution rate of 0.099. The hazardous waste disposal site was identified to be the most significant factor influencing Cr concentrations with a contribution rate of 0.100. Compared to the rest of the study area， the central region exhibited higher concentrations of Cd， Pb， and Cr， more complex human activity， and a greater number of high-high cluster zones， where Cd， Pb， and Cr concentrations and their most significant influencing factors were concentrated. When source prevention and control measures for heavy metal pollution in soil were implemented， particular attention should be prioritized towards the central region of the study area.
2025,
43(8):
204-213.
doi: 10.13205/j.hjgc.202508019
Abstract:
On the south side of Laoshan in Nanjing， the detected content of arsenic and cobalt in soils at different depths is abnormally high， with 12.15% and 12.97% of all sampling sites exceeding the screening value for Class I construction land， respectively. The exceedance points are distributed throughout the entire field， and the samples that exceed the standard are concentrated in the sedimentary and transported parent rock layers. Overall， the arsenic and cobalt content in the subsoil is higher than that in the topsoil. However， there are no traces of artificial pollution in the history of the plot. Based on the spatial distribution characteristics of arsenic and cobalt and the geological conditions of the plot， the high arsenic and cobalt content in the soil is identified related to the parent rock. The current soil environmental management system has some limitations in evaluating the high background of heavy metals at the plot scale. By employing statistical methods， the 95th percentile value was selected as the soil background value， thus it can provide a reference for the formulation of soil remediation targets in this study area. The method for identifying high-background soils and determining background values developed in this study facilitates more targeted investigation and remediation of sites with abnormally high heavy metal levels， thereby largely avoiding over-investigation and over-remediation.
On the south side of Laoshan in Nanjing， the detected content of arsenic and cobalt in soils at different depths is abnormally high， with 12.15% and 12.97% of all sampling sites exceeding the screening value for Class I construction land， respectively. The exceedance points are distributed throughout the entire field， and the samples that exceed the standard are concentrated in the sedimentary and transported parent rock layers. Overall， the arsenic and cobalt content in the subsoil is higher than that in the topsoil. However， there are no traces of artificial pollution in the history of the plot. Based on the spatial distribution characteristics of arsenic and cobalt and the geological conditions of the plot， the high arsenic and cobalt content in the soil is identified related to the parent rock. The current soil environmental management system has some limitations in evaluating the high background of heavy metals at the plot scale. By employing statistical methods， the 95th percentile value was selected as the soil background value， thus it can provide a reference for the formulation of soil remediation targets in this study area. The method for identifying high-background soils and determining background values developed in this study facilitates more targeted investigation and remediation of sites with abnormally high heavy metal levels， thereby largely avoiding over-investigation and over-remediation.
2025,
43(8):
214-225.
doi: 10.13205/j.hjgc.202508020
Abstract:
With the intensification of global climate change， a series of carbon-negative emissions technologies have been proposed. However， existing technologies primarily focus on the primary fixation of CO2. Humification， represented by coupling reactions， has long played a critical role in soil and sediment formation. Its ability to lock organic matter and pollutants holds potential as a core mechanism for carbon fixation and negative emissions by converting organic residues into stable humic substances， thereby influencing carbon storage and comprehensive climate effects. Iron minerals are pivotal in this process， interacting with organic carbon to promote humification and forming stable organo-mineral complexes that enhance long-term organic carbon sequestration. This review synthesizes the chemical mechanisms of iron mineral-facilitated humification and its impacts on carbon cycle， focusing on key reaction pathways such as the polyphenol pathway， Maillard reaction pathway， and the integrated polyphenol-Maillard pathway. The role of iron minerals in stabilizing soil organic carbon is also analyzed. Finally， the development of iron-mediated humification technologies for carbon-negative emissions is discussed， aiming to advance the application of coupling-enhanced humification in environmental remediation and carbon neutrality.
With the intensification of global climate change， a series of carbon-negative emissions technologies have been proposed. However， existing technologies primarily focus on the primary fixation of CO2. Humification， represented by coupling reactions， has long played a critical role in soil and sediment formation. Its ability to lock organic matter and pollutants holds potential as a core mechanism for carbon fixation and negative emissions by converting organic residues into stable humic substances， thereby influencing carbon storage and comprehensive climate effects. Iron minerals are pivotal in this process， interacting with organic carbon to promote humification and forming stable organo-mineral complexes that enhance long-term organic carbon sequestration. This review synthesizes the chemical mechanisms of iron mineral-facilitated humification and its impacts on carbon cycle， focusing on key reaction pathways such as the polyphenol pathway， Maillard reaction pathway， and the integrated polyphenol-Maillard pathway. The role of iron minerals in stabilizing soil organic carbon is also analyzed. Finally， the development of iron-mediated humification technologies for carbon-negative emissions is discussed， aiming to advance the application of coupling-enhanced humification in environmental remediation and carbon neutrality.
Analysis of CO2 emissions influencing factors in power sector across representative regions of China
2025,
43(8):
226-232.
doi: 10.13205/j.hjgc.202508021
Abstract:
Accurately quantifying CO2 emissions from the power sector and identifying heterogeneity across generation units are critical for formulating effective decarbonization strategies. Here， we apply the IPCC-recommended methodology to estimate unit-level CO2 emissions and intensities in representative Chinese provinces using detailed 2020 data on energy consumption， power generation， and heat output. A spatially disaggregated assessment across Shandong， Guangdong， Inner Mongolia， and Jiangsu—comprising 324 （489）， 123 （245）， 165 （255）， and 206 （276） plants （units）， respectively—reveals that Shandong and Jiangsu dominate in both electricity and heat supply， with coal-fired units remaining the primary contributors. In terms of absolute emissions， provinces with higher emission intensities also tend to contribute more to total CO2 emissions. Coal-fired generation accounts for the majority of emissions， with sub-400 MW units dominating in Shandong and Inner Mongolia， and units above 400 MW dominating in Guangdong and Jiangsu. Units aged 15 to 20 years emerge as the main emission sources， although newer units clearly outperform older ones in energy efficiency. Our findings show substantial variation in CO2 emission intensities across regions and unit types. Inner Mongolia and Shandong exhibit the highest intensities， while gas-fired units consistently outperform coal-fired units in emission performance. Larger units emit less CO2 per output unit， and newer installations demonstrate significantly lower intensities than the older counterparts. This study provides a robust empirical foundation to support differentiated control strategies in China’s power sector， with implications for both near-term policy interventions and long-term climate planning.
Accurately quantifying CO2 emissions from the power sector and identifying heterogeneity across generation units are critical for formulating effective decarbonization strategies. Here， we apply the IPCC-recommended methodology to estimate unit-level CO2 emissions and intensities in representative Chinese provinces using detailed 2020 data on energy consumption， power generation， and heat output. A spatially disaggregated assessment across Shandong， Guangdong， Inner Mongolia， and Jiangsu—comprising 324 （489）， 123 （245）， 165 （255）， and 206 （276） plants （units）， respectively—reveals that Shandong and Jiangsu dominate in both electricity and heat supply， with coal-fired units remaining the primary contributors. In terms of absolute emissions， provinces with higher emission intensities also tend to contribute more to total CO2 emissions. Coal-fired generation accounts for the majority of emissions， with sub-400 MW units dominating in Shandong and Inner Mongolia， and units above 400 MW dominating in Guangdong and Jiangsu. Units aged 15 to 20 years emerge as the main emission sources， although newer units clearly outperform older ones in energy efficiency. Our findings show substantial variation in CO2 emission intensities across regions and unit types. Inner Mongolia and Shandong exhibit the highest intensities， while gas-fired units consistently outperform coal-fired units in emission performance. Larger units emit less CO2 per output unit， and newer installations demonstrate significantly lower intensities than the older counterparts. This study provides a robust empirical foundation to support differentiated control strategies in China’s power sector， with implications for both near-term policy interventions and long-term climate planning.
2025,
43(8):
233-243.
doi: 10.13205/j.hjgc.202508022
Abstract:
Under the Dual Carbon Goals， systematically assessing the carbon footprint of China's bioeconomy is crucial for promoting sustainable development. This study employed an Environmentally-Extended Multi-Regional Input-Output （EE-MRIO） model， based on the EXIOBASE v3.9.6 database， to systematically analyze the global carbon footprint driven by China's final consumption in its bioeconomy from 2000 to 2022， including its spatio-temporal evolution and structural characteristics. The study found that the total global carbon footprint driven by China's final consumption in its bioeconomy is substantial and shows a fluctuating upward trend. Although the domestic carbon footprint has long dominated， the import carbon footprint significantly increased during the same period， indicating a growing environmental responsibility spillover effect. In terms of global carbon footprint composition， primary biotic resources have consistently been the largest source， accounting for over 60% in the long term. Bioenergy and waste valorization， along with related bio-based services， have also made prominent contributions. The domestic carbon emission structure has undergone a profound transformation. There has been an accelerated shift of hotspots from upstream sectors centered on agriculture and livestock to downstream sectors dominated by manufacturing and energy （including resource utilization）. The influence of emerging bio-based chemicals continues to grow， while biofuels show significant policy-related fluctuations. The sources of the import carbon footprint exhibit notable geographical concentration， meaning that resource-intensive， high-carbon-emission upstream production processes are mainly undertaken by developing countries， while developed countries focus more on high value-added processing and livestock product manufacturing. This study provides scientific support for the green transition of China's bioeconomy， the development of carbon accounting standards， and the coordinated governance of global supply chains.
Under the Dual Carbon Goals， systematically assessing the carbon footprint of China's bioeconomy is crucial for promoting sustainable development. This study employed an Environmentally-Extended Multi-Regional Input-Output （EE-MRIO） model， based on the EXIOBASE v3.9.6 database， to systematically analyze the global carbon footprint driven by China's final consumption in its bioeconomy from 2000 to 2022， including its spatio-temporal evolution and structural characteristics. The study found that the total global carbon footprint driven by China's final consumption in its bioeconomy is substantial and shows a fluctuating upward trend. Although the domestic carbon footprint has long dominated， the import carbon footprint significantly increased during the same period， indicating a growing environmental responsibility spillover effect. In terms of global carbon footprint composition， primary biotic resources have consistently been the largest source， accounting for over 60% in the long term. Bioenergy and waste valorization， along with related bio-based services， have also made prominent contributions. The domestic carbon emission structure has undergone a profound transformation. There has been an accelerated shift of hotspots from upstream sectors centered on agriculture and livestock to downstream sectors dominated by manufacturing and energy （including resource utilization）. The influence of emerging bio-based chemicals continues to grow， while biofuels show significant policy-related fluctuations. The sources of the import carbon footprint exhibit notable geographical concentration， meaning that resource-intensive， high-carbon-emission upstream production processes are mainly undertaken by developing countries， while developed countries focus more on high value-added processing and livestock product manufacturing. This study provides scientific support for the green transition of China's bioeconomy， the development of carbon accounting standards， and the coordinated governance of global supply chains.
2025,
43(8):
244-254.
doi: 10.13205/j.hjgc.202508023
Abstract:
The judicious distribution of finite water resources across various provinces and sectors， in conjunction with the mitigation of carbon emissions， is crucial for the synergistic advancement of water conservation and carbon reduction. This research established three distinct scenarios： a baseline scenario， a water intensity constraint scenario， and a water stress constraint scenario. By improving and employing an environmentally extended input-output model， this study quantified the impact of diverse water resource constraints on regional carbon emissions， thereby elucidating the intricate water-carbon interaction dynamics among different provinces under the auspices of water resource limitations.The findings of this study revealed that water resource constraints exerted a significant influence on production-based carbon emissions， leading to a nationwide reduction ranging from 0.1% to 15.5%. This reduction was attributed to the constraints' impact on sectoral production activities. Notably， the northwest region demonstratesd a comparatively higher reduction in carbon emissions per unit of economic loss， underscoring its potential as a key area for carbon reduction initiatives.In the realm of consumption-side carbon emissions， water resource constraints predominantly affected inter-regional trade activities. This influence diminishesd the responsibility borne by each province for the carbon emissions generated in other provinces， resulting in a reduction of consumption-based carbon emissions by 1.5% to 3.1%. Moreover， water resource constraints had a profound effect on regional carbon transfers through inter-regional trade networks. These constraints could alter the net direction of inter-regional embodied carbon transfers， effectively shifting the burden of carbon emissions from water-deficient western and northern regions to the relatively water-endowed southern regions. This shift showed the potential to mitigate the carbon emission pressure in the western and northern regions. Drawing upon the findings of this study as a practical reference， the development and coordinated implementation of appropriate policies are critical components in achieving the objectives of water conservation and carbon reduction. It is essential to first identify key and potential regions， and subsequently formulate differentiated regional policies for water conservation and carbon reduction based on local conditions. This approach will not only promote the synergy between water conservation and carbon reduction but also facilitate economic development.
The judicious distribution of finite water resources across various provinces and sectors， in conjunction with the mitigation of carbon emissions， is crucial for the synergistic advancement of water conservation and carbon reduction. This research established three distinct scenarios： a baseline scenario， a water intensity constraint scenario， and a water stress constraint scenario. By improving and employing an environmentally extended input-output model， this study quantified the impact of diverse water resource constraints on regional carbon emissions， thereby elucidating the intricate water-carbon interaction dynamics among different provinces under the auspices of water resource limitations.The findings of this study revealed that water resource constraints exerted a significant influence on production-based carbon emissions， leading to a nationwide reduction ranging from 0.1% to 15.5%. This reduction was attributed to the constraints' impact on sectoral production activities. Notably， the northwest region demonstratesd a comparatively higher reduction in carbon emissions per unit of economic loss， underscoring its potential as a key area for carbon reduction initiatives.In the realm of consumption-side carbon emissions， water resource constraints predominantly affected inter-regional trade activities. This influence diminishesd the responsibility borne by each province for the carbon emissions generated in other provinces， resulting in a reduction of consumption-based carbon emissions by 1.5% to 3.1%. Moreover， water resource constraints had a profound effect on regional carbon transfers through inter-regional trade networks. These constraints could alter the net direction of inter-regional embodied carbon transfers， effectively shifting the burden of carbon emissions from water-deficient western and northern regions to the relatively water-endowed southern regions. This shift showed the potential to mitigate the carbon emission pressure in the western and northern regions. Drawing upon the findings of this study as a practical reference， the development and coordinated implementation of appropriate policies are critical components in achieving the objectives of water conservation and carbon reduction. It is essential to first identify key and potential regions， and subsequently formulate differentiated regional policies for water conservation and carbon reduction based on local conditions. This approach will not only promote the synergy between water conservation and carbon reduction but also facilitate economic development.
2025,
43(8):
255-269.
doi: 10.13205/j.hjgc.202508024
Abstract:
As a core component of the national energy system， the power system plays a pivotal role in achieving the Dual Carbon Goals. Due to its long industrial chain and complex processes， the carbon emissions of the power industry exhibit distinct phase and structural characteristics. The life-cycle carbon footprint accounting method， which tracks greenhouse gas emissions throughout the entire process from resource extraction to end use， provides a theoretical foundation and data support for systematically identifying and optimizing carbon reduction pathways. Based on the fundamental concept of carbon footprint， this paper systematically reviewed typical carbon emission links on the power generation side and across the entire power system， and compared the application scenarios， advantages， and limitations of process analysis methods， input-output methods， and hybrid methods. Additionally， it evaluated the application features of mainstream databases and accounting tools such as ecoinvent， Sphera， and the International Energy Agency （IEA） in the analysis of power carbon footprints， conducted comparative analysis in terms of system boundary definition， model structure， and data sources. On this basis， in conjunction with key directions such as the development of multi-energy complementarity， the enhancement of regional adaptability， the refinement of data processing， and the establishment of accounting standard systems， this paper anticipated the future development direction of life-cycle carbon footprint accounting in the power system. The research findings can provide technical support for the refined management of carbon emissions in the power industry and offer decision-making references for the formulation of green and low-carbon policies and the selection of low-carbon technologies.
As a core component of the national energy system， the power system plays a pivotal role in achieving the Dual Carbon Goals. Due to its long industrial chain and complex processes， the carbon emissions of the power industry exhibit distinct phase and structural characteristics. The life-cycle carbon footprint accounting method， which tracks greenhouse gas emissions throughout the entire process from resource extraction to end use， provides a theoretical foundation and data support for systematically identifying and optimizing carbon reduction pathways. Based on the fundamental concept of carbon footprint， this paper systematically reviewed typical carbon emission links on the power generation side and across the entire power system， and compared the application scenarios， advantages， and limitations of process analysis methods， input-output methods， and hybrid methods. Additionally， it evaluated the application features of mainstream databases and accounting tools such as ecoinvent， Sphera， and the International Energy Agency （IEA） in the analysis of power carbon footprints， conducted comparative analysis in terms of system boundary definition， model structure， and data sources. On this basis， in conjunction with key directions such as the development of multi-energy complementarity， the enhancement of regional adaptability， the refinement of data processing， and the establishment of accounting standard systems， this paper anticipated the future development direction of life-cycle carbon footprint accounting in the power system. The research findings can provide technical support for the refined management of carbon emissions in the power industry and offer decision-making references for the formulation of green and low-carbon policies and the selection of low-carbon technologies.
2025,
43(8):
270-279.
doi: 10.13205/j.hjgc.202508025
Abstract:
To synchronously integrate digital twin and virtual reality （VR） technologies into the operation and construction of sewage treatment plants （STPs） and realize the highly intelligent development of STPs， a metaverse system for smart STPs supported by digital twin technology was constructed， and the optimal scheme for integrating virtual-physical interaction technology with smart STPs was explored. Specific implementation approaches are proposed as follows： a multi-modal architecture was adopted to develop digital twin models， a virtual interaction system， and the weighted regularized extreme learning machine （WRELM） deep learning mechanism. On the one hand， this integration serves for fully automated operation monitoring of STPs and the analysis and simulation of wastewater treatment data； on the other hand， it supports immersive virtual-physical interaction and intelligent operation of STPs. By comparing the applicability of various deep learning models in the platform， the WRELM was selected to establish an incremental learning mechanism for the metaverse platform， thereby achieving an optimal configuration of processes and unit parameters in wastewater treatment. This platform enables real-time monitoring， simulation， and immersive virtual-physical interaction of STPs. It not only effectively ensures the efficient and energy-saving operation of the wastewater treatment process， but also realizes intelligent data analysis， process digital twinning， autonomous virtual roaming， and automated fault handling in wastewater treatment.
To synchronously integrate digital twin and virtual reality （VR） technologies into the operation and construction of sewage treatment plants （STPs） and realize the highly intelligent development of STPs， a metaverse system for smart STPs supported by digital twin technology was constructed， and the optimal scheme for integrating virtual-physical interaction technology with smart STPs was explored. Specific implementation approaches are proposed as follows： a multi-modal architecture was adopted to develop digital twin models， a virtual interaction system， and the weighted regularized extreme learning machine （WRELM） deep learning mechanism. On the one hand， this integration serves for fully automated operation monitoring of STPs and the analysis and simulation of wastewater treatment data； on the other hand， it supports immersive virtual-physical interaction and intelligent operation of STPs. By comparing the applicability of various deep learning models in the platform， the WRELM was selected to establish an incremental learning mechanism for the metaverse platform， thereby achieving an optimal configuration of processes and unit parameters in wastewater treatment. This platform enables real-time monitoring， simulation， and immersive virtual-physical interaction of STPs. It not only effectively ensures the efficient and energy-saving operation of the wastewater treatment process， but also realizes intelligent data analysis， process digital twinning， autonomous virtual roaming， and automated fault handling in wastewater treatment.
2025,
43(8):
280-291.
doi: 10.13205/j.hjgc.202508026
Abstract:
The systematic resolution of black-odor water issues in urban and rural areas is a critical task in current efforts to improve ecological environmental quality. Faced with the complex water quality evolution patterns during the formation of black-odor water bodies， accurately understanding their formation mechanisms and implementing early intervention are key to achieving precise governance. This study analyzed 179 water bodies with varying pollution levels in Ningbo City， employing the three-dimensional fluorescence region integration （EEM-FRI） method to rapidly assess the composition of dissolved organic matter （DOM）. Principal component analysis combined with Kmeans clustering （PCA-Kmeans） and random forest （RF） machine learning algorithms were applied to analyze DOM data， exploring the formation mechanisms and key indicative factors of black-odor water bodies. The PCA-Kmeans model， based on EEM-FRI data， classified the samples into five groups according to pollution levels. The grouping results showed high consistency with the gradients of major pollutants， such as total nitrogen， total phosphorus， and COD. The accumulation of protein substances generated by microbial activity （ΦIV） was identified as a key indicative factor of pollution levels in black-odor water bodies. Meanwhile， anthropogenic protein substances （ΦI and ΦII） reflect the content of easily degradable organic carbon. Furthermore， the results of the RF model indicated that ΦIV had a significant impact on the formation of black-odor water bodies （with a SHAP value contribution ratio of 11.16%）. Its influence was more continuous and precise compared to that of total dissolved organic carbon （DOC）. Within the range of 0.9×106 < ΦIV < 1.0×106， SHAP values significantly increased from negative to positive， reflecting the important indicative significance of ΦIV for early detection of water body blackening and odor evolution. This study demonstrates that the combination of DOM data and machine learning can provide robust data support for the precise governance of black-odor water bodies.
The systematic resolution of black-odor water issues in urban and rural areas is a critical task in current efforts to improve ecological environmental quality. Faced with the complex water quality evolution patterns during the formation of black-odor water bodies， accurately understanding their formation mechanisms and implementing early intervention are key to achieving precise governance. This study analyzed 179 water bodies with varying pollution levels in Ningbo City， employing the three-dimensional fluorescence region integration （EEM-FRI） method to rapidly assess the composition of dissolved organic matter （DOM）. Principal component analysis combined with Kmeans clustering （PCA-Kmeans） and random forest （RF） machine learning algorithms were applied to analyze DOM data， exploring the formation mechanisms and key indicative factors of black-odor water bodies. The PCA-Kmeans model， based on EEM-FRI data， classified the samples into five groups according to pollution levels. The grouping results showed high consistency with the gradients of major pollutants， such as total nitrogen， total phosphorus， and COD. The accumulation of protein substances generated by microbial activity （ΦIV） was identified as a key indicative factor of pollution levels in black-odor water bodies. Meanwhile， anthropogenic protein substances （ΦI and ΦII） reflect the content of easily degradable organic carbon. Furthermore， the results of the RF model indicated that ΦIV had a significant impact on the formation of black-odor water bodies （with a SHAP value contribution ratio of 11.16%）. Its influence was more continuous and precise compared to that of total dissolved organic carbon （DOC）. Within the range of 0.9×106 < ΦIV < 1.0×106， SHAP values significantly increased from negative to positive， reflecting the important indicative significance of ΦIV for early detection of water body blackening and odor evolution. This study demonstrates that the combination of DOM data and machine learning can provide robust data support for the precise governance of black-odor water bodies.
