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2023 Vol. 41, No. 10
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2023, 41(10): 1-8,123.
doi: 10.13205/j.hjgc.202310001
Abstract:
The increase in carbon emissions accompanying the rapid growth of the world economy has led to global warming, which has aroused a high degree of concern in the international community and a soaring call for emissions reduction. Satellite remote sensing, with its macro, rapid, quantitative and accurate characteristics, is one of the most feasible and effective technical support for carbon monitoring. In order to solve the problem of low temporal resolution in the existing low-orbit atmospheric CO2 remote sensing detection, a Spatio-Temporal combined modulation spatial heterodyne Interferometric Imaging Spectroscopy technique (STIIS) for atmospheric CO2 column concentration detection in geostationary orbit is proposed. Based on STIIS, a high-orbit, high temporal and spatial resolution atmospheric CO2 monitor prototype has been developed, with O2-A 0.76 μm, CO2 weak absorption 1.57 μm and CO2 strong absorption 2.05 μm detection channels, and a spatial resolution of better than 3 km@36,000 km. The principle prototype can achieve CO2 detection accuracy better than 2×10-6 and time resolution better than 3.5 h covering China. Based on the principle prototype, the field test and flight test have been carried out, effectively inverting the CO2 concentration information of different target areas, verifying the technical feasibility of the high orbit high spatiotemporal resolution atmospheric carbon dioxide monitor, and providing a technical basis for the development and data application of the next-generation carbon monitoring payload.
The increase in carbon emissions accompanying the rapid growth of the world economy has led to global warming, which has aroused a high degree of concern in the international community and a soaring call for emissions reduction. Satellite remote sensing, with its macro, rapid, quantitative and accurate characteristics, is one of the most feasible and effective technical support for carbon monitoring. In order to solve the problem of low temporal resolution in the existing low-orbit atmospheric CO2 remote sensing detection, a Spatio-Temporal combined modulation spatial heterodyne Interferometric Imaging Spectroscopy technique (STIIS) for atmospheric CO2 column concentration detection in geostationary orbit is proposed. Based on STIIS, a high-orbit, high temporal and spatial resolution atmospheric CO2 monitor prototype has been developed, with O2-A 0.76 μm, CO2 weak absorption 1.57 μm and CO2 strong absorption 2.05 μm detection channels, and a spatial resolution of better than 3 km@36,000 km. The principle prototype can achieve CO2 detection accuracy better than 2×10-6 and time resolution better than 3.5 h covering China. Based on the principle prototype, the field test and flight test have been carried out, effectively inverting the CO2 concentration information of different target areas, verifying the technical feasibility of the high orbit high spatiotemporal resolution atmospheric carbon dioxide monitor, and providing a technical basis for the development and data application of the next-generation carbon monitoring payload.
2023, 41(10): 9-13.
doi: 10.13205/j.hjgc.202310002
Abstract:
Aiming at the problems of signal light intensity modulated laser heterodyne detection method used to detect the concentration/profile of atmospheric greenhouse gas column at present, such as signal light loss and complex spectral inversion model of local oscillator laser wavelength modulated laser heterodyne detection method, a laser heterodyne detection method based on local oscillator laser intensity modulation was proposed. In view of this, a dual-channel balanced heterodyne spectrum detection system with a 1.571 μm narrow linewidth semiconductor laser as a local oscillator light source was built by combining the balanced detection technology, and the system performance analysis and the measurement of atmospheric CO2 column abundance was carried out. Different from the laser heterodyne spectrum detection method reported in the current literature, the system used an optical switch to modulate the intensity of the local oscillator laser, and used balanced detection technology to eliminate the influence of background noise of local oscillator laser intensity. The results showed that compared with the traditional laser heterodyne detection method with signal light intensity modulation, this method can improve the signal-to-noise ratio of heterodyne signal by 3.7 times, and the corresponding measurement accuracy of CO2 column abundance was improved by 3.36 times. The above research shows that the laser heterodyne detection method based on local oscillator laser intensity modulation can effectively improve the performance of the laser heterodyne spectrum detection system.
Aiming at the problems of signal light intensity modulated laser heterodyne detection method used to detect the concentration/profile of atmospheric greenhouse gas column at present, such as signal light loss and complex spectral inversion model of local oscillator laser wavelength modulated laser heterodyne detection method, a laser heterodyne detection method based on local oscillator laser intensity modulation was proposed. In view of this, a dual-channel balanced heterodyne spectrum detection system with a 1.571 μm narrow linewidth semiconductor laser as a local oscillator light source was built by combining the balanced detection technology, and the system performance analysis and the measurement of atmospheric CO2 column abundance was carried out. Different from the laser heterodyne spectrum detection method reported in the current literature, the system used an optical switch to modulate the intensity of the local oscillator laser, and used balanced detection technology to eliminate the influence of background noise of local oscillator laser intensity. The results showed that compared with the traditional laser heterodyne detection method with signal light intensity modulation, this method can improve the signal-to-noise ratio of heterodyne signal by 3.7 times, and the corresponding measurement accuracy of CO2 column abundance was improved by 3.36 times. The above research shows that the laser heterodyne detection method based on local oscillator laser intensity modulation can effectively improve the performance of the laser heterodyne spectrum detection system.
2023, 41(10): 14-19,140.
doi: 10.13205/j.hjgc.202310003
Abstract:
Ground-based Fourier transform infrared spectroscopy (FTIR) has become an important technology for monitoring greenhouse gas column concentration, due to its high spatiotemporal resolution and sensitivity to changes in surface atmospheric concentrations. This paper was based on the solar spectrum observed by a portable Fourier transform infrared spectrometer (EM27/SUN) to retrieve the CO2 and CH4 total column concentrations in Beijing. By comparing with high-resolution FTIR observation, the accuracy and reliability of EM27/SUN observation were verified. At the same time, the observation accuracy was calculated by using the concentration results of CO2 and CH4 column observations at noon. The accuracy of CO2 was 0.16×10-6, and the accuracy of CH4 was 1.4×10-9. Finally, the time series changes of CO2 and CH4 were presented, and the variation of CO2 and CH4 were more consistent during the observation period. This study shows that portable FTIR can accurately determine CO2 and CH4 column concentration.
Ground-based Fourier transform infrared spectroscopy (FTIR) has become an important technology for monitoring greenhouse gas column concentration, due to its high spatiotemporal resolution and sensitivity to changes in surface atmospheric concentrations. This paper was based on the solar spectrum observed by a portable Fourier transform infrared spectrometer (EM27/SUN) to retrieve the CO2 and CH4 total column concentrations in Beijing. By comparing with high-resolution FTIR observation, the accuracy and reliability of EM27/SUN observation were verified. At the same time, the observation accuracy was calculated by using the concentration results of CO2 and CH4 column observations at noon. The accuracy of CO2 was 0.16×10-6, and the accuracy of CH4 was 1.4×10-9. Finally, the time series changes of CO2 and CH4 were presented, and the variation of CO2 and CH4 were more consistent during the observation period. This study shows that portable FTIR can accurately determine CO2 and CH4 column concentration.
2023, 41(10): 20-29,44.
doi: 10.13205/j.hjgc.202310004
Abstract:
In atmospheric environment monitoring, the measurement of stable gaseous isotopes can be used to evaluate atmospheric pollution with the benefit of prevention and control. In the context of achieving carbon peaking and carbon neutrality goals, the application of stable gaseous isotope measurement technology can help monitor the emission and absorption process of greenhouse gas, and its data can also help to support the development and optimization of emission reduction policies, thereby the promotion on the implementation of carbon reduction efforts can be achieved. The analytical methods with value assignment technique are the key factors to the analysis of isotopes, by focusing on this subject, the research progress of stable gaseous isotopes is summarized, which contains three instrumental techniques:mass spectrometry, gas chromatography, and spectroscopy (especially FTIR), and the relating mathematical model is also introduced. Additionally, the advantages and disadvantages of the three techniques with their applicability for the analysis of stable gaseous isotopes are described, which can provide positive support for studying carbon source & sink and chemical analysis.
In atmospheric environment monitoring, the measurement of stable gaseous isotopes can be used to evaluate atmospheric pollution with the benefit of prevention and control. In the context of achieving carbon peaking and carbon neutrality goals, the application of stable gaseous isotope measurement technology can help monitor the emission and absorption process of greenhouse gas, and its data can also help to support the development and optimization of emission reduction policies, thereby the promotion on the implementation of carbon reduction efforts can be achieved. The analytical methods with value assignment technique are the key factors to the analysis of isotopes, by focusing on this subject, the research progress of stable gaseous isotopes is summarized, which contains three instrumental techniques:mass spectrometry, gas chromatography, and spectroscopy (especially FTIR), and the relating mathematical model is also introduced. Additionally, the advantages and disadvantages of the three techniques with their applicability for the analysis of stable gaseous isotopes are described, which can provide positive support for studying carbon source & sink and chemical analysis.
2023, 41(10): 30-36.
doi: 10.13205/j.hjgc.202310005
Abstract:
Carbon dioxide (CO2) is the main greenhouse gas that causes the greenhouse effect, and satellite remote sensing provides a new technique for CO2 monitoring. Based on the carbon dioxide column average dry air mixing ratio (XCO2) data and mathematical statistical methods, such as autoregressive model, spatial autocorrelation, and hot and cold spot analysis, we analyzed the spatiotemporal evolution characteristics of XCO2 concentration in Hebei Province. Pearson correlation coefficient and geographical weighted regression were used to analyze the driving effects of natural and human activities on XCO2 concentration. The results were as follows:1) the average monthly XCO2 concentration in Hebei province varies periodically, reaching a peak in April and a trough in August. In addition, it is higher in winter and spring, lower in summer and autumn. 2) the time series of XCO2 concentration in Hebei province has autocorrelation, and the best lag time is 2 months. 3) the concentration of XCO2 in Hebei province is significantly positively correlated in space. The overall distribution pattern of XCO2 concentration was lower in the northwest and higher in the southeast, and clustered high-value regions were formed in southern, central, and northern Hebei. 4) geography, vegetation and meteorology have significant negative effects on XCO2 concentration. The tri-variate combination of digital elevation model (DEM), normalized difference vegetation index (NDVI) and precipitation and the bivariate combination of precipitation and DEM can better explain XCO2 concentration. The study showed that CO2 concentration can be inhibited by increasing vegetation cover and precipitation, and it can provide a scientific reference for carbon emission reduction measures in Hebei Province.
Carbon dioxide (CO2) is the main greenhouse gas that causes the greenhouse effect, and satellite remote sensing provides a new technique for CO2 monitoring. Based on the carbon dioxide column average dry air mixing ratio (XCO2) data and mathematical statistical methods, such as autoregressive model, spatial autocorrelation, and hot and cold spot analysis, we analyzed the spatiotemporal evolution characteristics of XCO2 concentration in Hebei Province. Pearson correlation coefficient and geographical weighted regression were used to analyze the driving effects of natural and human activities on XCO2 concentration. The results were as follows:1) the average monthly XCO2 concentration in Hebei province varies periodically, reaching a peak in April and a trough in August. In addition, it is higher in winter and spring, lower in summer and autumn. 2) the time series of XCO2 concentration in Hebei province has autocorrelation, and the best lag time is 2 months. 3) the concentration of XCO2 in Hebei province is significantly positively correlated in space. The overall distribution pattern of XCO2 concentration was lower in the northwest and higher in the southeast, and clustered high-value regions were formed in southern, central, and northern Hebei. 4) geography, vegetation and meteorology have significant negative effects on XCO2 concentration. The tri-variate combination of digital elevation model (DEM), normalized difference vegetation index (NDVI) and precipitation and the bivariate combination of precipitation and DEM can better explain XCO2 concentration. The study showed that CO2 concentration can be inhibited by increasing vegetation cover and precipitation, and it can provide a scientific reference for carbon emission reduction measures in Hebei Province.
2023, 41(10): 37-44.
doi: 10.13205/j.hjgc.202310006
Abstract:
Increasing carbon dioxide (CO2) exacerbated the greenhouse effect, which threatened human normal lives. Currently, the equipment used for CO2 monitoring is heavy and expensive. Therefore, the study designed a portable CO2 detector based on non-dispersive infrared (NDIR) optical absorption principles. Considering on-site monitoring, the sensor was susceptible to the interference of relative humidity (RH). Therefore, the effect of RH on the response of the sensor was studied, and the response characteristics of the sensor at different RH levels were understood. Through the combination of concentration and humidity changes, the correlation (R2) of the secondary function of the correlation was above 0.94. Through continuous outdoor monitoring and comparison with the standard reference instruments, it was found that there was a better correlation between the calibrated data and the standard reference instrument, with R2 increasing from 0.62 to 0.73 before calibration, to 0.83 to 0.97. The cluster analysis of sensors can greatly reduce the error caused by individual differences in sensors, thus improving the accuracy of data. It was found that through analysis of multiple sensor data combinations, the average relative deviation decreased with the increase in the number of sensors, and the minimum average relative deviation was only 1.4%.
Increasing carbon dioxide (CO2) exacerbated the greenhouse effect, which threatened human normal lives. Currently, the equipment used for CO2 monitoring is heavy and expensive. Therefore, the study designed a portable CO2 detector based on non-dispersive infrared (NDIR) optical absorption principles. Considering on-site monitoring, the sensor was susceptible to the interference of relative humidity (RH). Therefore, the effect of RH on the response of the sensor was studied, and the response characteristics of the sensor at different RH levels were understood. Through the combination of concentration and humidity changes, the correlation (R2) of the secondary function of the correlation was above 0.94. Through continuous outdoor monitoring and comparison with the standard reference instruments, it was found that there was a better correlation between the calibrated data and the standard reference instrument, with R2 increasing from 0.62 to 0.73 before calibration, to 0.83 to 0.97. The cluster analysis of sensors can greatly reduce the error caused by individual differences in sensors, thus improving the accuracy of data. It was found that through analysis of multiple sensor data combinations, the average relative deviation decreased with the increase in the number of sensors, and the minimum average relative deviation was only 1.4%.
2023, 41(10): 45-50,68.
doi: 10.13205/j.hjgc.202310007
Abstract:
The commonly used CO2 detection instrument for the measurement of CO2 flux at the water-air interface of natural water is based on the long light path infrared absorption method, which is bulky and expensive. To reduce the research cost and the mass and volume of the detection equipment, a low-cost CO2 sensor based on the principle of non-dispersive infrared (NDIR) was deployed in the flux box to build a low-cost CO2 detection instrument, and the in-situ detection contrast experiment with the referred instrument was carried out. The results showed that, CO2 volume fraction detection results of the low-cost CO2 detector showed a good correlation (R2=0.86) with the referred instrument, and the relative deviation range of the low-cost detector was -1.45% to 0.92%. According to the low-cost detector results, the CO2 flux at each detection point was estimated in the range of 7.76 to 15.93 mmol/(m2/d), and its change rule was the same as that of the referred instrument. Environmental relative humidity and voltage drift of the sensor itself were the main sources of CO2 flux detection deviation. The interference of these two factors could be reduced by humidity correction and increasing the number of CO2 sensors in the flux box. This low-cost detector has a good application prospect in measuring the CO2 flux of reservoirs.
The commonly used CO2 detection instrument for the measurement of CO2 flux at the water-air interface of natural water is based on the long light path infrared absorption method, which is bulky and expensive. To reduce the research cost and the mass and volume of the detection equipment, a low-cost CO2 sensor based on the principle of non-dispersive infrared (NDIR) was deployed in the flux box to build a low-cost CO2 detection instrument, and the in-situ detection contrast experiment with the referred instrument was carried out. The results showed that, CO2 volume fraction detection results of the low-cost CO2 detector showed a good correlation (R2=0.86) with the referred instrument, and the relative deviation range of the low-cost detector was -1.45% to 0.92%. According to the low-cost detector results, the CO2 flux at each detection point was estimated in the range of 7.76 to 15.93 mmol/(m2/d), and its change rule was the same as that of the referred instrument. Environmental relative humidity and voltage drift of the sensor itself were the main sources of CO2 flux detection deviation. The interference of these two factors could be reduced by humidity correction and increasing the number of CO2 sensors in the flux box. This low-cost detector has a good application prospect in measuring the CO2 flux of reservoirs.
2023, 41(10): 51-60.
doi: 10.13205/j.hjgc.202310008
Abstract:
As the core infrastructure for urban construction, the wastewater treatment plant is also a considerable contributor to greenhouse gas emissions. However, the inaccurate emission data and the inability to locate the emission sources have hindered the reduction progress of pollution and carbon emissions. To address this problem, the use of monitoring tools to obtain on-site data is needed to correct and support the current emission reduction efforts of greenhouse gases, while helping the wastewater treatment plants achieve a green transformation. A review of the recent full-scale greenhouse gas emission monitoring campaigns was conducted. The monitoring method system was summarized, the existing monitoring data from the perspective of process characteristics was analyzed, and the main influencing factors of greenhouse gas emissions were identified. Meanwhile, a reference for further research and method standardization on greenhouse gas monitoring in wastewater treatment was proposed.
As the core infrastructure for urban construction, the wastewater treatment plant is also a considerable contributor to greenhouse gas emissions. However, the inaccurate emission data and the inability to locate the emission sources have hindered the reduction progress of pollution and carbon emissions. To address this problem, the use of monitoring tools to obtain on-site data is needed to correct and support the current emission reduction efforts of greenhouse gases, while helping the wastewater treatment plants achieve a green transformation. A review of the recent full-scale greenhouse gas emission monitoring campaigns was conducted. The monitoring method system was summarized, the existing monitoring data from the perspective of process characteristics was analyzed, and the main influencing factors of greenhouse gas emissions were identified. Meanwhile, a reference for further research and method standardization on greenhouse gas monitoring in wastewater treatment was proposed.
2023, 41(10): 61-68.
doi: 10.13205/j.hjgc.202310009
Abstract:
Offshore geological storage can realize the long-term isolation of a large amount of carbon dioxide from the atmosphere, which is crucial for realizing carbon neutrality, especially in the coastal industrial developed areas. Although the possibility of leakage is extremely small, monitoring CO2 leakage from marine geological storage is still necessary and important for the evaluation and application of this artificial carbon sink. Well-known simulated leak experiments include QICS, ETI-MMV, and STEM-CCS, which validate a series of technical approaches, while the related research is in the initial stage in China. According to the typical application scenarios of offshore geological CO2 storage, the development status of leakage monitoring technology is outlined, and the leakage monitoring strategy is proposed including:establishing a solid marine environmental baseline, carrying out a risk assessment, and developing a marine monitoring plan for a specific location. According to the study, CO2 leakage monitoring during offshore geological storage needs to develop multi-level monitoring technology for long-term, low-cost, real-time monitoring and accurate evaluation. We should establish and improve a systematic, intelligent monitoring, early warning, and disposal system based on a clear mechanism of leakage risk points, combined with the baseline investigation of the marine environment.
Offshore geological storage can realize the long-term isolation of a large amount of carbon dioxide from the atmosphere, which is crucial for realizing carbon neutrality, especially in the coastal industrial developed areas. Although the possibility of leakage is extremely small, monitoring CO2 leakage from marine geological storage is still necessary and important for the evaluation and application of this artificial carbon sink. Well-known simulated leak experiments include QICS, ETI-MMV, and STEM-CCS, which validate a series of technical approaches, while the related research is in the initial stage in China. According to the typical application scenarios of offshore geological CO2 storage, the development status of leakage monitoring technology is outlined, and the leakage monitoring strategy is proposed including:establishing a solid marine environmental baseline, carrying out a risk assessment, and developing a marine monitoring plan for a specific location. According to the study, CO2 leakage monitoring during offshore geological storage needs to develop multi-level monitoring technology for long-term, low-cost, real-time monitoring and accurate evaluation. We should establish and improve a systematic, intelligent monitoring, early warning, and disposal system based on a clear mechanism of leakage risk points, combined with the baseline investigation of the marine environment.
2023, 41(10): 69-81.
doi: 10.13205/j.hjgc.202310010
Abstract:
CO2 leakage is the main factor threatening the long-term safe operation of CCUS geological storage projects, and an effective safety monitoring system is the basis of project risk management and decision-making. CO2 mainly escapes through three pathways:wellbore, cap rock, fault, or fracture. As different pathways pose different leakage characteristics, different monitoring methods are required. Based on the principles, advantages, disadvantages, and development trends, three main monitoring methods, i.e. CO2 environment monitoring, safety monitoring, and migration monitoring were summarized, covering the following monitoring technologies, such as atmosphere, near surface, shallow formation, surface deformation, geological deformation, ground stress, well integrity, wellbore corrosion, and underground CO2 migration. The development of monitoring technologies for typical projects, at home and abroad was summarized, which can provide reference for future carbon sequestration engineering projects in China. It is difficult for a single monitoring technology to meet total engineering needs. High-precision in-situ online environmental monitoring, integrated with intelligent safety monitoring, and long-term continuous dynamic migration monitoring is expected to become future research's focus. It is suggested to build a monitoring system integrating spatial horizon and safe migration, based on high precision, low cost, and real-time monitoring.
CO2 leakage is the main factor threatening the long-term safe operation of CCUS geological storage projects, and an effective safety monitoring system is the basis of project risk management and decision-making. CO2 mainly escapes through three pathways:wellbore, cap rock, fault, or fracture. As different pathways pose different leakage characteristics, different monitoring methods are required. Based on the principles, advantages, disadvantages, and development trends, three main monitoring methods, i.e. CO2 environment monitoring, safety monitoring, and migration monitoring were summarized, covering the following monitoring technologies, such as atmosphere, near surface, shallow formation, surface deformation, geological deformation, ground stress, well integrity, wellbore corrosion, and underground CO2 migration. The development of monitoring technologies for typical projects, at home and abroad was summarized, which can provide reference for future carbon sequestration engineering projects in China. It is difficult for a single monitoring technology to meet total engineering needs. High-precision in-situ online environmental monitoring, integrated with intelligent safety monitoring, and long-term continuous dynamic migration monitoring is expected to become future research's focus. It is suggested to build a monitoring system integrating spatial horizon and safe migration, based on high precision, low cost, and real-time monitoring.
2023, 41(10): 82-90.
doi: 10.13205/j.hjgc.202310011
Abstract:
Nitrous oxide(N2O), the third largest anthropogenic emissive greenhouse gas, can cause ozone layer depletion and contribute to climate warming, and is one of the pollutants generated in chemical production. Adipic acid and nitric acid production produce large amounts of N2O, which have been considered as main sources of N2O emission in the chemical industry. However, the sources of N2O emission are not limited to the nitric and adipic acid production processes. Due to low concentrations of N2O emissions, no clear emission standards or monitoring methods, some other chemical processes have been neglected. This paper systematically elucidated the existence of N2O emission sources and the mechanisms of N2O generation in the chemical production processes, and clarified the N2O emission problems that have not yet received much attention in the chemical industry. Moreover, by summarizing the causes of N2O emission and mechanisms of N2O formation during those well-known processes, this paper speculated on other processes with potential N2O emission. Additionally, this paper introduced the N2O abatement technologies and corresponding problems that have been applied in the industry. Analyzed the effect of N2O emission reduction in industrial applications and the current development predicament combined with the evaluation of abatement technologies, and proposed the future research focus of N2O emission reduction technologies. To provide a reference and practical direction for the effective control and emission reduction of N2O in human production activities.
Nitrous oxide(N2O), the third largest anthropogenic emissive greenhouse gas, can cause ozone layer depletion and contribute to climate warming, and is one of the pollutants generated in chemical production. Adipic acid and nitric acid production produce large amounts of N2O, which have been considered as main sources of N2O emission in the chemical industry. However, the sources of N2O emission are not limited to the nitric and adipic acid production processes. Due to low concentrations of N2O emissions, no clear emission standards or monitoring methods, some other chemical processes have been neglected. This paper systematically elucidated the existence of N2O emission sources and the mechanisms of N2O generation in the chemical production processes, and clarified the N2O emission problems that have not yet received much attention in the chemical industry. Moreover, by summarizing the causes of N2O emission and mechanisms of N2O formation during those well-known processes, this paper speculated on other processes with potential N2O emission. Additionally, this paper introduced the N2O abatement technologies and corresponding problems that have been applied in the industry. Analyzed the effect of N2O emission reduction in industrial applications and the current development predicament combined with the evaluation of abatement technologies, and proposed the future research focus of N2O emission reduction technologies. To provide a reference and practical direction for the effective control and emission reduction of N2O in human production activities.
2023, 41(10): 91-98,162.
doi: 10.13205/j.hjgc.202310012
Abstract:
The utilization of muck soil has become one of the key tasks in green shield construction. In order to further quantify the carbon emission reduction potential of the resource utilization of the muck soil from tunnel shield construction, the carbon emission estimation method for the whole process of subgrade filling with improved muck soil from tunnel shield construction was established. Based on the actual engineering data of the Shunde Waterway Tunnel Project of the West Extension Line of Jihua Road, the difference in carbon emissions between the utilization of muck soil and conventional landfill treatment was analyzed. In addition, the sensitivity analysis of carbon emissions in the whole process of residue utilization was also carried out. The results showed that:carbon emissions from the utilization of muck soil mainly occur in the muck soil improvement and transportation stage, while those of the conventional landfill treatment mainly occur in the generation stage; the total discharge of muck soil from the Project is about 65029 m3, of which 53162 m3 can be used for muck soil improvement. If totally landfilled, the CO2 emission during the whole life cycle is about 1.84×106 kg CO2eq, occupying 11058 m2 of landfill site; the CBR of muck soil improved by 3% lime+3% desulfurized gypsum can reach 141.4, and the rebound modulus is 198.8 MPa. The CO2 produced in the whole process is 8.89×105 kg CO2eq more than that produced by conventional treatment of muck soil; the CBR of muck soil improved by 3% carbide slag+6% fly ash can reach 89.06, and the rebound modulus is 158.9 MPa. The CO2 produced in the whole process is 6.44×105 kg CO2eq less than that produced by conventional treatment of muck soil.
The utilization of muck soil has become one of the key tasks in green shield construction. In order to further quantify the carbon emission reduction potential of the resource utilization of the muck soil from tunnel shield construction, the carbon emission estimation method for the whole process of subgrade filling with improved muck soil from tunnel shield construction was established. Based on the actual engineering data of the Shunde Waterway Tunnel Project of the West Extension Line of Jihua Road, the difference in carbon emissions between the utilization of muck soil and conventional landfill treatment was analyzed. In addition, the sensitivity analysis of carbon emissions in the whole process of residue utilization was also carried out. The results showed that:carbon emissions from the utilization of muck soil mainly occur in the muck soil improvement and transportation stage, while those of the conventional landfill treatment mainly occur in the generation stage; the total discharge of muck soil from the Project is about 65029 m3, of which 53162 m3 can be used for muck soil improvement. If totally landfilled, the CO2 emission during the whole life cycle is about 1.84×106 kg CO2eq, occupying 11058 m2 of landfill site; the CBR of muck soil improved by 3% lime+3% desulfurized gypsum can reach 141.4, and the rebound modulus is 198.8 MPa. The CO2 produced in the whole process is 8.89×105 kg CO2eq more than that produced by conventional treatment of muck soil; the CBR of muck soil improved by 3% carbide slag+6% fly ash can reach 89.06, and the rebound modulus is 158.9 MPa. The CO2 produced in the whole process is 6.44×105 kg CO2eq less than that produced by conventional treatment of muck soil.
2023, 41(10): 99-107,172.
doi: 10.13205/j.hjgc.202310013
Abstract:
In order to provide the carbon emission data of the loess tunnel construction stage, seek the carbon emission reduction path of loess tunnel, and help realize the low-carbon construction of loess tunnel, we carry out the calculation and analysis of carbon emission during the construction stage of loess tunnel, which is of great significance. Taking the SJ Loess Tunnel as the research object, a life cycle assessment was used to calculate the carbon emission during the construction stage, the carbon emission characteristics were analyzed and summarized from the three aspects of the carbon emission source, time and space, and suggestions on carbon emissions reduction during the loess tunnel construction stage were put forward. The results showed that:1) carbon emission in the construction stage of SJ Loess Tunnel was 2.6769 million tons and carbon emission intensity was 60.16 t CO2eq/m; 2) carbon emissions were highest in the stage of material production and processing. Cement and reinforcement were the key sources of carbon emissions in loess tunnels. Secondary lining, surrounding rock support, and temporary support were the top three subprojects in terms of carbon emissions in the construction stage. 3) The carbon emission intensity of loess tunnel was higher than that of the rock tunnel, and the carbon emission distribution of material energy and sub-projects was similar.
In order to provide the carbon emission data of the loess tunnel construction stage, seek the carbon emission reduction path of loess tunnel, and help realize the low-carbon construction of loess tunnel, we carry out the calculation and analysis of carbon emission during the construction stage of loess tunnel, which is of great significance. Taking the SJ Loess Tunnel as the research object, a life cycle assessment was used to calculate the carbon emission during the construction stage, the carbon emission characteristics were analyzed and summarized from the three aspects of the carbon emission source, time and space, and suggestions on carbon emissions reduction during the loess tunnel construction stage were put forward. The results showed that:1) carbon emission in the construction stage of SJ Loess Tunnel was 2.6769 million tons and carbon emission intensity was 60.16 t CO2eq/m; 2) carbon emissions were highest in the stage of material production and processing. Cement and reinforcement were the key sources of carbon emissions in loess tunnels. Secondary lining, surrounding rock support, and temporary support were the top three subprojects in terms of carbon emissions in the construction stage. 3) The carbon emission intensity of loess tunnel was higher than that of the rock tunnel, and the carbon emission distribution of material energy and sub-projects was similar.
2023, 41(10): 108-116.
doi: 10.13205/j.hjgc.202310014
Abstract:
Taking the construction industry of Shandong Province as the research object, this paper analyzes the relevant influencing factors of carbon emissions through the logarithmic mean Divisia index (LMDI) model, builds a system dynamics model, and predicts the impact of four different scenarios on carbon emissions by regulating GDP growth rate, the proportions of building materials and carbon trading policy. The results show that:1) the economic benefit effect and indirect carbon emission intensity are the main driving factors affecting the carbon emissions of the construction industry in Shandong Province. 2) Under the single policy simulation scenario, the growth trend of the total carbon emissions of the construction industry in Shandong Province has slowed down year by year. Compared with the economic growth and carbon trading policy adjustment, the contribution of the improvement of the building materials structure to the emission reduction is higher. 3) Under the comprehensive regulation and control plan, the carbon emission intensity of the construction industry of Shandong Province in 2030 will decrease by 64.34% compared with 2006, which reaches the national goal of reducing the carbon emission intensity by 60% in 2030.
Taking the construction industry of Shandong Province as the research object, this paper analyzes the relevant influencing factors of carbon emissions through the logarithmic mean Divisia index (LMDI) model, builds a system dynamics model, and predicts the impact of four different scenarios on carbon emissions by regulating GDP growth rate, the proportions of building materials and carbon trading policy. The results show that:1) the economic benefit effect and indirect carbon emission intensity are the main driving factors affecting the carbon emissions of the construction industry in Shandong Province. 2) Under the single policy simulation scenario, the growth trend of the total carbon emissions of the construction industry in Shandong Province has slowed down year by year. Compared with the economic growth and carbon trading policy adjustment, the contribution of the improvement of the building materials structure to the emission reduction is higher. 3) Under the comprehensive regulation and control plan, the carbon emission intensity of the construction industry of Shandong Province in 2030 will decrease by 64.34% compared with 2006, which reaches the national goal of reducing the carbon emission intensity by 60% in 2030.
2023, 41(10): 117-123.
doi: 10.13205/j.hjgc.202310015
Abstract:
In order to promote the leading role of campus in carbon neutralization action and take the lead in realizing campus carbon neutralization, based on the data of energy monitoring center, the carbon emission of a university building in Jiangxi Province from 2017 to 2021 was measured, the STIRPAT model of carbon emission of campus building operation was constructed, and three scenarios of baseline scenarios, low carbon scenarios and ultra-low carbon scenarios were set by scenario analysis method, to forecast the changing trend of carbon emission of campus building operation phase from 2022 to 2035, and targeted carbon reduction suggestions were put forward. Results showed that the carbon emissions of campus buildings showed significant seasonal characteristics. Dormitory carbon emissions were the main source of carbon emissions in the operation stage of campus buildings, mainly functional carbon emissions. Energy structure and energy consumption intensity had a greater impact on carbon emissions, while the number of energy users and building area had a relatively smaller impact. Under both low-carbon scenario and ultra-low-carbon scenario, the carbon peak in the campus can all be achieved before 2030.
In order to promote the leading role of campus in carbon neutralization action and take the lead in realizing campus carbon neutralization, based on the data of energy monitoring center, the carbon emission of a university building in Jiangxi Province from 2017 to 2021 was measured, the STIRPAT model of carbon emission of campus building operation was constructed, and three scenarios of baseline scenarios, low carbon scenarios and ultra-low carbon scenarios were set by scenario analysis method, to forecast the changing trend of carbon emission of campus building operation phase from 2022 to 2035, and targeted carbon reduction suggestions were put forward. Results showed that the carbon emissions of campus buildings showed significant seasonal characteristics. Dormitory carbon emissions were the main source of carbon emissions in the operation stage of campus buildings, mainly functional carbon emissions. Energy structure and energy consumption intensity had a greater impact on carbon emissions, while the number of energy users and building area had a relatively smaller impact. Under both low-carbon scenario and ultra-low-carbon scenario, the carbon peak in the campus can all be achieved before 2030.
2023, 41(10): 124-132.
doi: 10.13205/j.hjgc.202310016
Abstract:
Aiming at the volatility and nonlinearity of transport carbon emission data series, a combined prediction model combining variational mode decomposition (VMD), sparrow search algorithm (SSA), and least square support vector machine (LSSVM) was adopted to predict transport carbon emission more accurately. Firstly, the VMD method was used to decompose the original carbon emission data series into multiple low-complexity, stable modal components, and a residual term to reduce the volatility and nonlinearity of the carbon emission data series. Secondly, the LSSVM model was established for each decomposition module, and the parameters of the LSSVM model were optimized by SSA. Finally, the prediction results of each module were integrated and superimposed to obtain the final carbon emission prediction results. The carbon emission data of China's transportation industry from 1990 to 2019 were calculated to verify the model and compare it with various models. The results showed that the root-mean-mean error, mean absolute error, mean absolute percentage error, determination coefficient and Nash coefficient of the VMD-SSA-LSSVM model was 6.28 million t, 5.74 million t, 0.73%, 0.998 and 0.996, respectively, which was superior to other models, indicating that the model can effectively improve the prediction accuracy.
Aiming at the volatility and nonlinearity of transport carbon emission data series, a combined prediction model combining variational mode decomposition (VMD), sparrow search algorithm (SSA), and least square support vector machine (LSSVM) was adopted to predict transport carbon emission more accurately. Firstly, the VMD method was used to decompose the original carbon emission data series into multiple low-complexity, stable modal components, and a residual term to reduce the volatility and nonlinearity of the carbon emission data series. Secondly, the LSSVM model was established for each decomposition module, and the parameters of the LSSVM model were optimized by SSA. Finally, the prediction results of each module were integrated and superimposed to obtain the final carbon emission prediction results. The carbon emission data of China's transportation industry from 1990 to 2019 were calculated to verify the model and compare it with various models. The results showed that the root-mean-mean error, mean absolute error, mean absolute percentage error, determination coefficient and Nash coefficient of the VMD-SSA-LSSVM model was 6.28 million t, 5.74 million t, 0.73%, 0.998 and 0.996, respectively, which was superior to other models, indicating that the model can effectively improve the prediction accuracy.
2023, 41(10): 133-140.
doi: 10.13205/j.hjgc.202310017
Abstract:
Urban rail transit projects consume a substantial amount of energy and generate significant quantities of solid waste and carbon dioxide during their construction and operation. It is imperative to address carbon emissions in urban rail transit projects without delay. This study drew upon research achievements on carbon emissions from urban rail transit projects, both domestically and internationally, and identified the three primary factors that influence carbon emissions in such projects:economy, technology, and institutions. Twelve factors, including fixed asset investment in urban rail transit, maturity of low-carbon technologies, and stringency of environmental regulations, were selected as observable indicators. Then a survey questionnaire was carefully designed to conduct empirical research. By employing the partial least squares structural equation model, we analyzed the causal pathways and impacts of these influencing factors on carbon emissions in urban rail transit projects. The research found that both economic systems and technological factors have significant impacts on the carbon emissions of urban rail transit projects (0.433, 0.317, 0.224). Therefore, achieving carbon reduction in urban rail transit projects requires the joint participation of economic systems and economic factors. Moreover, economic factors have a significant influence on both systems and technologies (0.748, 0.514). Therefore, it is important to focus on increasing investment in economic elements to achieve carbon reduction in urban rail transit projects. Moreover, we provide actionable recommendations, with significant reference value for achieving energy efficiency and emission reduction in urban rail transit systems.
Urban rail transit projects consume a substantial amount of energy and generate significant quantities of solid waste and carbon dioxide during their construction and operation. It is imperative to address carbon emissions in urban rail transit projects without delay. This study drew upon research achievements on carbon emissions from urban rail transit projects, both domestically and internationally, and identified the three primary factors that influence carbon emissions in such projects:economy, technology, and institutions. Twelve factors, including fixed asset investment in urban rail transit, maturity of low-carbon technologies, and stringency of environmental regulations, were selected as observable indicators. Then a survey questionnaire was carefully designed to conduct empirical research. By employing the partial least squares structural equation model, we analyzed the causal pathways and impacts of these influencing factors on carbon emissions in urban rail transit projects. The research found that both economic systems and technological factors have significant impacts on the carbon emissions of urban rail transit projects (0.433, 0.317, 0.224). Therefore, achieving carbon reduction in urban rail transit projects requires the joint participation of economic systems and economic factors. Moreover, economic factors have a significant influence on both systems and technologies (0.748, 0.514). Therefore, it is important to focus on increasing investment in economic elements to achieve carbon reduction in urban rail transit projects. Moreover, we provide actionable recommendations, with significant reference value for achieving energy efficiency and emission reduction in urban rail transit systems.
2023, 41(10): 141-149.
doi: 10.13205/j.hjgc.202310018
Abstract:
A model based on LASSO regression (LASSO), Grey Wolf Optimization algorithm (GWO) and nuclear Extreme Learning Machine (KELM) was established to improve the prediction accuracy of industrial carbon emissions. Firstly, the direct and indirect carbon emissions of the industry during 2000 to 2020 were calculated according to the IPCC formula method and electrothermal allocation method respectively, and the gross domestic product (GDP), energy structure, and fixed asset investment were selected by the STIRPAT model. Then seven significant influencing factors were selected by grey correlation analysis and the LASSO regression model. Secondly, the parameter data of the industrial carbon emission system were preprocessed and input into the KELM model, and the KELM regularization coefficient (C) and kernel function parameter (γ) were optimized using the GWO algorithm. Finally, the forecast results were integrated and summarized, and the forecast results of LASSO-GGO-KELM, LASSO-SSA-KELM, LASSO-SFO-KELM, LASSO-KELM, and LASSO-ELM were compared and analyzed. The results showed that the predicted value of the LASSO-GGO-KELM model fits well with the actual value, and its mean square error, mean absolute error, root mean square error, and mean absolute percentage error was 0.02%, 1.09%, 1.33%, and 1.17% respectively, which was superior to other models, proving that this model can predict industrial carbon emissions more accurately. This study can provide a reference for China to realize the Double Carbon Goal as soon as possible.
A model based on LASSO regression (LASSO), Grey Wolf Optimization algorithm (GWO) and nuclear Extreme Learning Machine (KELM) was established to improve the prediction accuracy of industrial carbon emissions. Firstly, the direct and indirect carbon emissions of the industry during 2000 to 2020 were calculated according to the IPCC formula method and electrothermal allocation method respectively, and the gross domestic product (GDP), energy structure, and fixed asset investment were selected by the STIRPAT model. Then seven significant influencing factors were selected by grey correlation analysis and the LASSO regression model. Secondly, the parameter data of the industrial carbon emission system were preprocessed and input into the KELM model, and the KELM regularization coefficient (C) and kernel function parameter (γ) were optimized using the GWO algorithm. Finally, the forecast results were integrated and summarized, and the forecast results of LASSO-GGO-KELM, LASSO-SSA-KELM, LASSO-SFO-KELM, LASSO-KELM, and LASSO-ELM were compared and analyzed. The results showed that the predicted value of the LASSO-GGO-KELM model fits well with the actual value, and its mean square error, mean absolute error, root mean square error, and mean absolute percentage error was 0.02%, 1.09%, 1.33%, and 1.17% respectively, which was superior to other models, proving that this model can predict industrial carbon emissions more accurately. This study can provide a reference for China to realize the Double Carbon Goal as soon as possible.
2023, 41(10): 150-162.
doi: 10.13205/j.hjgc.202310019
Abstract:
Based on data from China's manufacturing sector spanning from 2000 to 2020, a two-dimensional decoupling model was constructed to assess the decoupling status of economic growth and carbon emissions. Additionally, a decoupling effort model was applied to investigate the driving mechanisms of decoupling. The research results were as follows:1) the total carbon emissions across 26 industries increased from 176.09×107 tons in 2000 to 639.21×107 tons in 2020, marking a growth of 3.63 times over 21 years. 2) across the 26 industries, there were a total of 18 types of two-dimensional decoupling states. The proportion of strong decoupling increased from 15.38% in the latter half of the "10th Five-Year Plan" period, to 40.77% in the "13th Five-Year Plan" period. While the decoupling status gradually improved, significant room for decoupling remained. 3) labor-intensive industries were more likely to achieve low economic levels with strong decoupling, capital-intensive industries were more likely to achieve moderate economic levels with weak decoupling, and technology-intensive industries were more likely to achieve low economic levels with weak decoupling. 4) the average decoupling effort indices for energy intensity and industrial structure were 0.686 and 0.031, respectively, making them key drivers for carbon decoupling in the manufacturing sector. However, the average decoupling effort index for energy structure was -0.147, which hindered the decoupling process. 5) among the industries, labor-intensive industries made the greatest decoupling efforts, followed by capital-intensive industries, while technology-intensive industries made the smallest efforts.
Based on data from China's manufacturing sector spanning from 2000 to 2020, a two-dimensional decoupling model was constructed to assess the decoupling status of economic growth and carbon emissions. Additionally, a decoupling effort model was applied to investigate the driving mechanisms of decoupling. The research results were as follows:1) the total carbon emissions across 26 industries increased from 176.09×107 tons in 2000 to 639.21×107 tons in 2020, marking a growth of 3.63 times over 21 years. 2) across the 26 industries, there were a total of 18 types of two-dimensional decoupling states. The proportion of strong decoupling increased from 15.38% in the latter half of the "10th Five-Year Plan" period, to 40.77% in the "13th Five-Year Plan" period. While the decoupling status gradually improved, significant room for decoupling remained. 3) labor-intensive industries were more likely to achieve low economic levels with strong decoupling, capital-intensive industries were more likely to achieve moderate economic levels with weak decoupling, and technology-intensive industries were more likely to achieve low economic levels with weak decoupling. 4) the average decoupling effort indices for energy intensity and industrial structure were 0.686 and 0.031, respectively, making them key drivers for carbon decoupling in the manufacturing sector. However, the average decoupling effort index for energy structure was -0.147, which hindered the decoupling process. 5) among the industries, labor-intensive industries made the greatest decoupling efforts, followed by capital-intensive industries, while technology-intensive industries made the smallest efforts.
SPATIAL AND TEMPORAL EVOLUTION OF CARBON STORAGE IN POYANG LAKE BASIN BASED ON PLUS AND INVEST MODEL
2023, 41(10): 163-172.
doi: 10.13205/j.hjgc.202310020
Abstract:
Urban expansion and land use change cause changes in the habitat and carbon storage in the Poyang Lake basin. In order to investigate the impact of land use replacement on carbon storage, this study took the Poyang Lake Basin as the research area, and applied InVEST model and PLUS model to simulate and predict the spatiotemporal distribution characteristics of land use and carbon storage, in order to explore the spatiotemporal evolution and response mechanism of carbon storage under natural development, urban construction, and ecological protection scenarios. The results showed that:1) the water area and construction land in Poyang Lake basin expanded from 2000 to 2020, while the land carbon storage continued to decline. 2) under the natural development and urban development scenarios, the amount of forest land, grassland and unused land decreased; under the ecological protection scenario, the amount of grassland decreased while forest land expanded; under the natural development scenario, cultivated land expanded. 3) compared with 2020, carbon storage in 2030 under natural development, urban development and ecological protection scenarios decreased by 190000 tons, decreased by 1.49 million tons and increased by 250000 tons, respectively, and land use distribution showed a high consistency with carbon storage distribution. Exploring the spatio-temporal evolution of land use and carbon storage in Poyang Lake Basin, and anticipating future trend, is conducive to the sustainable development of land use in the basin, and provides a reference for realizing the Dual Carbon goal in the Poyang Lake basin.
Urban expansion and land use change cause changes in the habitat and carbon storage in the Poyang Lake basin. In order to investigate the impact of land use replacement on carbon storage, this study took the Poyang Lake Basin as the research area, and applied InVEST model and PLUS model to simulate and predict the spatiotemporal distribution characteristics of land use and carbon storage, in order to explore the spatiotemporal evolution and response mechanism of carbon storage under natural development, urban construction, and ecological protection scenarios. The results showed that:1) the water area and construction land in Poyang Lake basin expanded from 2000 to 2020, while the land carbon storage continued to decline. 2) under the natural development and urban development scenarios, the amount of forest land, grassland and unused land decreased; under the ecological protection scenario, the amount of grassland decreased while forest land expanded; under the natural development scenario, cultivated land expanded. 3) compared with 2020, carbon storage in 2030 under natural development, urban development and ecological protection scenarios decreased by 190000 tons, decreased by 1.49 million tons and increased by 250000 tons, respectively, and land use distribution showed a high consistency with carbon storage distribution. Exploring the spatio-temporal evolution of land use and carbon storage in Poyang Lake Basin, and anticipating future trend, is conducive to the sustainable development of land use in the basin, and provides a reference for realizing the Dual Carbon goal in the Poyang Lake basin.
2023, 41(10): 173-184.
doi: 10.13205/j.hjgc.202310021
Abstract:
In the critical period of synergy between pollution reduction and carbon reduction in the 14th Five-Year Plan, the sewage treatment system is facing the problems of low accuracy and timeliness of carbon accounting, and the lack of effective analysis and evaluation tools for carbon emissions. Based on the pollutant reduction ratio of different processes of urban and industrial sewage treatment plants in Suzhou from 2001 to 2020, the time series data of localized carbon emission factors and carbon emissions were weighted and established. An extended Kaya identity factorization model of carbon emission in sewage treatment systems is constructed to quantitatively analyze the influence of seven factors, including technical effect, energy effect, sewage discharge intensity, industrial structure, economic effect, urbanization rate, and population size, on the change of carbon emission in sewage treatment plants. The study showed that carbon emissions of the sewage treatment system in Suzhou from 2001 to 2020 were 4306.6082 million tons of CO2-eq, CO2 was the main contributor, and the carbon emission factor was 1.3687~1.9499 kg CO2-eq/m3; reducing CO2 emissions from the sewage treatment process, and N2O emissions from the sludge disposal process was the breakthrough point; the economy and population scale effects were the main factors to promote the carbon emission increment of urban and industrial sewage treatment plants, respectively, and the technical effect was the decisive factor to restrain the reduction. Finally, suggestions are put forward, including the reduction of carbon emission in urban sewage treatment plants, the improvement of low energy consumption of sewage and low N2O emission technology of sludge, the promotion of water saving in the whole society, and the promotion of industrial upgrading by technological innovation.
In the critical period of synergy between pollution reduction and carbon reduction in the 14th Five-Year Plan, the sewage treatment system is facing the problems of low accuracy and timeliness of carbon accounting, and the lack of effective analysis and evaluation tools for carbon emissions. Based on the pollutant reduction ratio of different processes of urban and industrial sewage treatment plants in Suzhou from 2001 to 2020, the time series data of localized carbon emission factors and carbon emissions were weighted and established. An extended Kaya identity factorization model of carbon emission in sewage treatment systems is constructed to quantitatively analyze the influence of seven factors, including technical effect, energy effect, sewage discharge intensity, industrial structure, economic effect, urbanization rate, and population size, on the change of carbon emission in sewage treatment plants. The study showed that carbon emissions of the sewage treatment system in Suzhou from 2001 to 2020 were 4306.6082 million tons of CO2-eq, CO2 was the main contributor, and the carbon emission factor was 1.3687~1.9499 kg CO2-eq/m3; reducing CO2 emissions from the sewage treatment process, and N2O emissions from the sludge disposal process was the breakthrough point; the economy and population scale effects were the main factors to promote the carbon emission increment of urban and industrial sewage treatment plants, respectively, and the technical effect was the decisive factor to restrain the reduction. Finally, suggestions are put forward, including the reduction of carbon emission in urban sewage treatment plants, the improvement of low energy consumption of sewage and low N2O emission technology of sludge, the promotion of water saving in the whole society, and the promotion of industrial upgrading by technological innovation.
2023, 41(10): 185-194.
doi: 10.13205/j.hjgc.202310022
Abstract:
The effects of single-species (Cynodon dactylon) and mixed-species (Schefflera and Cynodon dactylon) on methane emission reduction in landfill cover were studied under the drying-wetting cycles. Three-layer landfill cover consists of a machine-made sand tailing mixed with 10% bentonite, gravel layer and silty sand layer. A total of four drying-wetting cycle tests were conducted. Throughout these tests, measurements were taken for plant growth, soil moisture content, and gas pressure changes in each layer of soil column. Additionally, gas concentration of each component was monitored. The results demonstrate that landfill gas inhibited the plants growth, causing leaf wilting and reduced photosynthesis and transpiration rates. The mixed-species group showed a higher methane oxidation capacity, compared to the single-species group. The methane removal rate of the single-species was approximately 69%~89% of that observed in the mixed-species group. The difference in methane oxidation capacity between the mixed-species group and single-species group increased with the increase in drying-wetting cycle number. After four drying-wetting cycles, both the mixed-species and single-species group showed a decrease in methane oxidation capacity. The mixed-species group reached approximate 45% of its peak value from the first drought, while the single-species group achieved around 34% of its initial peak value. Plant roots enhanced the soil's methane oxidation capacity, and the methane oxidation capacity inside the root zone was greater than that outside the root zone. This study elucidated the influence of plant combinations on methane oxidation under drying-wetting cycles. The test results provide a solid basis for mitigating methane emissions in landfills.
The effects of single-species (Cynodon dactylon) and mixed-species (Schefflera and Cynodon dactylon) on methane emission reduction in landfill cover were studied under the drying-wetting cycles. Three-layer landfill cover consists of a machine-made sand tailing mixed with 10% bentonite, gravel layer and silty sand layer. A total of four drying-wetting cycle tests were conducted. Throughout these tests, measurements were taken for plant growth, soil moisture content, and gas pressure changes in each layer of soil column. Additionally, gas concentration of each component was monitored. The results demonstrate that landfill gas inhibited the plants growth, causing leaf wilting and reduced photosynthesis and transpiration rates. The mixed-species group showed a higher methane oxidation capacity, compared to the single-species group. The methane removal rate of the single-species was approximately 69%~89% of that observed in the mixed-species group. The difference in methane oxidation capacity between the mixed-species group and single-species group increased with the increase in drying-wetting cycle number. After four drying-wetting cycles, both the mixed-species and single-species group showed a decrease in methane oxidation capacity. The mixed-species group reached approximate 45% of its peak value from the first drought, while the single-species group achieved around 34% of its initial peak value. Plant roots enhanced the soil's methane oxidation capacity, and the methane oxidation capacity inside the root zone was greater than that outside the root zone. This study elucidated the influence of plant combinations on methane oxidation under drying-wetting cycles. The test results provide a solid basis for mitigating methane emissions in landfills.
2023, 41(10): 195-203,244.
doi: 10.13205/j.hjgc.202310023
Abstract:
The change of carbon emissions affects the realization of China's carbon peaking and neutrality goals. Study on the influencing factors of carbon emissions is an important part. Currently, scholars at home and abroad have conducted a lot of research on the influencing factors of carbon emissions, involving national, regional, and provincial levels, and industry levels, but those factors have the complexity of spatial and temporal dimensions. The importance of influencing factors of carbon emissions changes dynamically with space and time, and the identification methods for the factors have different applicability, and the changing influencing factors of carbon emissions put forward higher requirements for the identification methods. These characteristics lead to the large and complex research results of existing carbon emissions research and lack of systematic combing. Therefore, from the perspective of identification content and theory, this paper comprehensively analyzes the influencing factors of carbon emissions and the change process of the identification methods, determines the main influencing factors of China's carbon emissions, and puts forward trend and directions for the further research.
The change of carbon emissions affects the realization of China's carbon peaking and neutrality goals. Study on the influencing factors of carbon emissions is an important part. Currently, scholars at home and abroad have conducted a lot of research on the influencing factors of carbon emissions, involving national, regional, and provincial levels, and industry levels, but those factors have the complexity of spatial and temporal dimensions. The importance of influencing factors of carbon emissions changes dynamically with space and time, and the identification methods for the factors have different applicability, and the changing influencing factors of carbon emissions put forward higher requirements for the identification methods. These characteristics lead to the large and complex research results of existing carbon emissions research and lack of systematic combing. Therefore, from the perspective of identification content and theory, this paper comprehensively analyzes the influencing factors of carbon emissions and the change process of the identification methods, determines the main influencing factors of China's carbon emissions, and puts forward trend and directions for the further research.
2023, 41(10): 204-212,222.
doi: 10.13205/j.hjgc.202310024
Abstract:
Increasing urban energy consumption and carbon dioxide emissions pose serious challenges to regional emission reduction policies. Based on the remote sensing simulation of night lights from 2004 to 2020, this study inverted the carbon emission data of 29 cities in North China, and used spatial autocorrelation and spatial Markov chain to analyze the spatial distribution characteristics of carbon emissions from the perspective of cities in North China from the dynamic and static aspects, to explore the agglomeration effect between cities; at the same time, in order to further clarify the factors affecting carbon emissions, based on the weighted regression model of time, space and geography, this study quantitatively identifies the relevant factors affecting urban carbon emissions from the aspects of the economy, society, environment and policy, and discusses the spatial heterogeneity can provide a theoretical basis for differentiated emission reduction. The results shows that the growth rate of per capita carbon emissions in North China is gradually decreasing, and there are obvious clustering characteristics between those cities. The influence of various factors on carbon emissions of cities in North China in different periods shows temporal and spatial heterogeneity. The level of economic development and industrial structure are strong driving forces to promote carbon emissions. Government policies have the most obvious inhibitory effect on carbon emissions. The urbanization rate and climate have the characteristics of first promoting and then inhibiting the production of carbon emissions.
Increasing urban energy consumption and carbon dioxide emissions pose serious challenges to regional emission reduction policies. Based on the remote sensing simulation of night lights from 2004 to 2020, this study inverted the carbon emission data of 29 cities in North China, and used spatial autocorrelation and spatial Markov chain to analyze the spatial distribution characteristics of carbon emissions from the perspective of cities in North China from the dynamic and static aspects, to explore the agglomeration effect between cities; at the same time, in order to further clarify the factors affecting carbon emissions, based on the weighted regression model of time, space and geography, this study quantitatively identifies the relevant factors affecting urban carbon emissions from the aspects of the economy, society, environment and policy, and discusses the spatial heterogeneity can provide a theoretical basis for differentiated emission reduction. The results shows that the growth rate of per capita carbon emissions in North China is gradually decreasing, and there are obvious clustering characteristics between those cities. The influence of various factors on carbon emissions of cities in North China in different periods shows temporal and spatial heterogeneity. The level of economic development and industrial structure are strong driving forces to promote carbon emissions. Government policies have the most obvious inhibitory effect on carbon emissions. The urbanization rate and climate have the characteristics of first promoting and then inhibiting the production of carbon emissions.
2023, 41(10): 213-222.
doi: 10.13205/j.hjgc.202310025
Abstract:
The western region of China is a vital node of the "Silk Road Economic Belt", with significant transportation status and huge pressure on transportation carbon emissions reduction. It is of great significance to deeply study the transportation carbon emission problem in the western region. Firstly, based on the provincial panel data of the transportation industry in the western region from 2000 to 2019, the top-down method was used to calculate transportation carbon emissions, and the spatial and temporal characteristics were described and analyzed by GIS software. Secondly, the LMDI decomposition method was used to explore the influencing factors and effects of transportation carbon emissions. Finally, the Tapio decoupling model was constructed to analyze the decoupling relationship between transportation carbon emissions and the economic development of the transportation industry in the western region. The results showed that:from 2000 to 2019, the total transportation carbon emissions in the western region showed an upward trend, increasing by about 4.6 times, while the overall growth rate decreased. The provinces with the highest transportation carbon emissions were gradually shifting to the southwestern region, and the cumulative increase of transportation carbon emissions in Sichuan ranked first in the western region, reaching 24.1485 million tons of CO2. Economic scale was the leading factor in promoting the growth of transportation carbon emissions in the western region, and the cumulative contribution rate of transportation carbon emissions was 87.9%, while the carbon emission factor contributes the least, with a cumulative contribution rate of only 4.4%. The energy consumption per unit turnover effect and transportation intensity on transportation carbon emissions were heterogeneous, and the industrial structure had an overall inhibitory effect on transportation carbon emissions. In addition, the overall development direction of the transportation industry in the western region tends to be low carbonization, and transportation carbon emissions and economic development of transportation industry in most provinces has experienced a trend from negative decoupling to expansion connection, and then to weak decoupling. Based on this, it was suggested to formulate a differentiated transportation carbon emission reduction path plan scheme, focus on preventing high carbon emission tendencies in resource-based areas, and strengthen regular monitoring and evaluation of carbon emissions in the transportation industry in the western region.
The western region of China is a vital node of the "Silk Road Economic Belt", with significant transportation status and huge pressure on transportation carbon emissions reduction. It is of great significance to deeply study the transportation carbon emission problem in the western region. Firstly, based on the provincial panel data of the transportation industry in the western region from 2000 to 2019, the top-down method was used to calculate transportation carbon emissions, and the spatial and temporal characteristics were described and analyzed by GIS software. Secondly, the LMDI decomposition method was used to explore the influencing factors and effects of transportation carbon emissions. Finally, the Tapio decoupling model was constructed to analyze the decoupling relationship between transportation carbon emissions and the economic development of the transportation industry in the western region. The results showed that:from 2000 to 2019, the total transportation carbon emissions in the western region showed an upward trend, increasing by about 4.6 times, while the overall growth rate decreased. The provinces with the highest transportation carbon emissions were gradually shifting to the southwestern region, and the cumulative increase of transportation carbon emissions in Sichuan ranked first in the western region, reaching 24.1485 million tons of CO2. Economic scale was the leading factor in promoting the growth of transportation carbon emissions in the western region, and the cumulative contribution rate of transportation carbon emissions was 87.9%, while the carbon emission factor contributes the least, with a cumulative contribution rate of only 4.4%. The energy consumption per unit turnover effect and transportation intensity on transportation carbon emissions were heterogeneous, and the industrial structure had an overall inhibitory effect on transportation carbon emissions. In addition, the overall development direction of the transportation industry in the western region tends to be low carbonization, and transportation carbon emissions and economic development of transportation industry in most provinces has experienced a trend from negative decoupling to expansion connection, and then to weak decoupling. Based on this, it was suggested to formulate a differentiated transportation carbon emission reduction path plan scheme, focus on preventing high carbon emission tendencies in resource-based areas, and strengthen regular monitoring and evaluation of carbon emissions in the transportation industry in the western region.
2023, 41(10): 223-229.
doi: 10.13205/j.hjgc.202310026
Abstract:
In order to scientifically evaluate the carbon neutrality capability of provinces, this paper proposed an evaluation index system for China's provincial carbon neutrality capability based on the energy-economy-environment (3E) system and proposed an improved TOPSIS method. Through the analysis and evaluation of the carbon neutrality capacity of 30 provinces in China, the effectiveness of the proposed method was verified, and suggestions were provided for provincial governments to achieve carbon neutrality. The results showed that the improved TOPSIS method can effectively evaluate the carbon neutrality capacity of a region; the impact of the economy is stronger than energy and environment, on achieving carbon neutrality; according to the sorting of carbon neutrality capability, we can divide the 30 provinces into outstanding provinces, relatively balanced provinces and some backward provinces. Among them, the provinces with prominent advantages are mainly economically developed provinces, while some underdeveloped provinces are mainly traditional resource-based provinces. Each type of province has different paths to enhance carbon neutrality capabilities.
In order to scientifically evaluate the carbon neutrality capability of provinces, this paper proposed an evaluation index system for China's provincial carbon neutrality capability based on the energy-economy-environment (3E) system and proposed an improved TOPSIS method. Through the analysis and evaluation of the carbon neutrality capacity of 30 provinces in China, the effectiveness of the proposed method was verified, and suggestions were provided for provincial governments to achieve carbon neutrality. The results showed that the improved TOPSIS method can effectively evaluate the carbon neutrality capacity of a region; the impact of the economy is stronger than energy and environment, on achieving carbon neutrality; according to the sorting of carbon neutrality capability, we can divide the 30 provinces into outstanding provinces, relatively balanced provinces and some backward provinces. Among them, the provinces with prominent advantages are mainly economically developed provinces, while some underdeveloped provinces are mainly traditional resource-based provinces. Each type of province has different paths to enhance carbon neutrality capabilities.
2023, 41(10): 230-244.
doi: 10.13205/j.hjgc.202310027
Abstract:
Cities are the main source of global greenhouse gas emissions. Due to the complexity of urban ecosystems and the uncertainty of human activities, there are significant differences in carbon cycling characteristics among different cities. Currently, carbon flux monitoring methods mainly include "bottom-up" and "top-down" approaches. However, there are few reports on the review of their analytical framework and monitoring methods. This paper provides a systematic review of urban carbon cycle influencing factors, carbon flux observation, and simulation methods, and introduces typical urban carbon monitoring networks both domestically and internationally. It also points out future research directions, including developing high-resolution global carbon assimilation theories and technologies, verifying and promoting the distribution characteristics of carbon sources/sinks in different urban land surfaces, tracing the anthropogenic carbon emissions and ecosystem carbon source/sink patterns in urban regions, and carrying out research on the exchange mechanisms between complex urban land surfaces and the atmosphere, as well as the environmental response mechanisms of urban carbon flux. This study will increase our understanding of the global carbon cycle and provide scientific support for China to implement the dual-carbon strategy, address climate negotiations and carbon inventory, and evaluate carbon neutrality.
Cities are the main source of global greenhouse gas emissions. Due to the complexity of urban ecosystems and the uncertainty of human activities, there are significant differences in carbon cycling characteristics among different cities. Currently, carbon flux monitoring methods mainly include "bottom-up" and "top-down" approaches. However, there are few reports on the review of their analytical framework and monitoring methods. This paper provides a systematic review of urban carbon cycle influencing factors, carbon flux observation, and simulation methods, and introduces typical urban carbon monitoring networks both domestically and internationally. It also points out future research directions, including developing high-resolution global carbon assimilation theories and technologies, verifying and promoting the distribution characteristics of carbon sources/sinks in different urban land surfaces, tracing the anthropogenic carbon emissions and ecosystem carbon source/sink patterns in urban regions, and carrying out research on the exchange mechanisms between complex urban land surfaces and the atmosphere, as well as the environmental response mechanisms of urban carbon flux. This study will increase our understanding of the global carbon cycle and provide scientific support for China to implement the dual-carbon strategy, address climate negotiations and carbon inventory, and evaluate carbon neutrality.
2023, 41(10): 245-252,259.
doi: 10.13205/j.hjgc.202310028
Abstract:
The article developed an inter-regional energy carbon emission cluster model (IRCE) based on trade activities to assess the impact of inter-regional trade on carbon emissions in Shanghai. It was found that Shanghai was an externally oriented city, and the impact of carbon emissions from out-of-province intermediate use was greater than the impact of carbon emissions from out-of-province intermediate inputs and final consumption. Zhejiang Province, Guangdong Province and Jiangsu Province were the provinces with the highest carbon emissions from out-of-province intermediate use in Shanghai. Shandong Province, Heilongjiang Province, Tianjin City and Inner Mongolia Autonomous Region were the most critical provinces for overall out-of-province intermediate inputs. From a sectoral perspective, the metal smelting and rolling processing sector, and the production and supply of electricity and heat sector were the more important sectors in out-of-province intermediate use and out-of-province intermediate input, respectively.
The article developed an inter-regional energy carbon emission cluster model (IRCE) based on trade activities to assess the impact of inter-regional trade on carbon emissions in Shanghai. It was found that Shanghai was an externally oriented city, and the impact of carbon emissions from out-of-province intermediate use was greater than the impact of carbon emissions from out-of-province intermediate inputs and final consumption. Zhejiang Province, Guangdong Province and Jiangsu Province were the provinces with the highest carbon emissions from out-of-province intermediate use in Shanghai. Shandong Province, Heilongjiang Province, Tianjin City and Inner Mongolia Autonomous Region were the most critical provinces for overall out-of-province intermediate inputs. From a sectoral perspective, the metal smelting and rolling processing sector, and the production and supply of electricity and heat sector were the more important sectors in out-of-province intermediate use and out-of-province intermediate input, respectively.
2023, 41(10): 253-259.
doi: 10.13205/j.hjgc.202310029
Abstract:
Municipal solid waste (MSW) treatment and disposal units are an important source of greenhouse gas (GHG) emissions. Clarifying the trend and characteristics of its emissions changes and reducing its GHG emissions is of great significance for alleviating the global greenhouse effect and helping China achieve the goals of "carbon peaking" and "carbon neutrality". The IPCC inventory model was used to evaluate the GHG emissions from MSW treatment and disposal units in Qingdao from 2010 to 2020. The results showed that GHG emission from MSW in Qingdao increased from 1.131 million tons CO2-eq in 2010 to 2.238 million tons CO2-eq in 2016, and decreased to 0.950 tons CO2-eq in 2020; the most emitted GHG, changed from CH4 from MWS landfills before 2019, to CO2 from MSW incinerator in 2020. As the proportion of incineration increased, the share of incineration GHG emissions in Qingdao reached 72.8% in 2020. Achieving domestic waste classification, improving landfill gas (LFG) collection efficiency, strengthening methane (CH4) oxidation in the cover layer, changing domestic waste disposal methods from landfill to incineration, and improving recyclable waste recovery efficiency are effective measures to achieve GHG emission reduction from MSW disposal units.
Municipal solid waste (MSW) treatment and disposal units are an important source of greenhouse gas (GHG) emissions. Clarifying the trend and characteristics of its emissions changes and reducing its GHG emissions is of great significance for alleviating the global greenhouse effect and helping China achieve the goals of "carbon peaking" and "carbon neutrality". The IPCC inventory model was used to evaluate the GHG emissions from MSW treatment and disposal units in Qingdao from 2010 to 2020. The results showed that GHG emission from MSW in Qingdao increased from 1.131 million tons CO2-eq in 2010 to 2.238 million tons CO2-eq in 2016, and decreased to 0.950 tons CO2-eq in 2020; the most emitted GHG, changed from CH4 from MWS landfills before 2019, to CO2 from MSW incinerator in 2020. As the proportion of incineration increased, the share of incineration GHG emissions in Qingdao reached 72.8% in 2020. Achieving domestic waste classification, improving landfill gas (LFG) collection efficiency, strengthening methane (CH4) oxidation in the cover layer, changing domestic waste disposal methods from landfill to incineration, and improving recyclable waste recovery efficiency are effective measures to achieve GHG emission reduction from MSW disposal units.
