Login
Register
E-alert
Current Articles
2025, Volume 43, Issue 4
Display Method:
2025,
43(4):
1-16.
doi: 10.13205/j.hjgc.202504001
Abstract:
Although urban areas account for less than 3% of the global land surface area， their direct and indirect CO2 emissions account for more than 70% of the global CO2 emissions from energy use. Moreover， ecosystems in urban areas have direct sink increase and indirect emission reduction effects. This paper summarized the domestic and foreign research progress about monitoring of carbon sources and sinks in urban areas. The monitoring technology of carbon sources and sinks in urban areas includes in-situ monitoring and remote sensing monitoring methods （ground-based remote sensing and satellite remote sensing）. In addition， many cities worldwide have already begun to establish urban greenhouse gas monitoring networks. The estimation methods of anthropogenic carbon emission sources and urban ecosystem carbon flux in urban areas mainly include "bottom-up" （mainly including inventory method， sample plot inventory method， eddy correlation method， model simulation method， etc.） and "top-down" method （mainly including carbon assimilation inversion method， etc.）. The research results on anthropogenic carbon sources and ecosystem carbon flux in urban areas were summarized. Finally， new monitoring methods and estimation methods were prospected to better serve the "dual carbon" goals. It could also provide an important scientific basis for predicting atmospheric CO2 content and the global warming trend. In the future， the "top-down" and "bottom-up" methods can be better combined， satellite remote sensing monitoring， ground observation， model assimilation and other methods can be coordinated， and multi-source data can be further integrated based on artificial intelligence， big data and other technologies to obtain more monitoring data of urban carbon emissions and carbon sinks with high spatial and temporal resolution， and the accuracy of assimilation inversion can be improved. In order to provide a more scientific basis for estimating anthropogenic carbon emission sources and ecosystem carbon flux in urban fine spatial scale.
Although urban areas account for less than 3% of the global land surface area， their direct and indirect CO2 emissions account for more than 70% of the global CO2 emissions from energy use. Moreover， ecosystems in urban areas have direct sink increase and indirect emission reduction effects. This paper summarized the domestic and foreign research progress about monitoring of carbon sources and sinks in urban areas. The monitoring technology of carbon sources and sinks in urban areas includes in-situ monitoring and remote sensing monitoring methods （ground-based remote sensing and satellite remote sensing）. In addition， many cities worldwide have already begun to establish urban greenhouse gas monitoring networks. The estimation methods of anthropogenic carbon emission sources and urban ecosystem carbon flux in urban areas mainly include "bottom-up" （mainly including inventory method， sample plot inventory method， eddy correlation method， model simulation method， etc.） and "top-down" method （mainly including carbon assimilation inversion method， etc.）. The research results on anthropogenic carbon sources and ecosystem carbon flux in urban areas were summarized. Finally， new monitoring methods and estimation methods were prospected to better serve the "dual carbon" goals. It could also provide an important scientific basis for predicting atmospheric CO2 content and the global warming trend. In the future， the "top-down" and "bottom-up" methods can be better combined， satellite remote sensing monitoring， ground observation， model assimilation and other methods can be coordinated， and multi-source data can be further integrated based on artificial intelligence， big data and other technologies to obtain more monitoring data of urban carbon emissions and carbon sinks with high spatial and temporal resolution， and the accuracy of assimilation inversion can be improved. In order to provide a more scientific basis for estimating anthropogenic carbon emission sources and ecosystem carbon flux in urban fine spatial scale.
2025,
43(4):
17-25.
doi: 10.13205/j.hjgc.202504002
Abstract:
The chemical industry park has the characteristics of large scale， gathering enterprises， dense distribution， large quantity of dangerous chemicals， complex production process and so on. Based on the air pollutant monitoring network consists of 30 micro air quality monitoring stations， a long-term monitoring study of air pollutants in a fine chemical industrial park in Zhejiang province was carried out. The results showed that： 1） O3 concentration showed a bimodal change， and the peak appeared in April （83.8 μg/m3） and September （84.5 μg/m3）. The concentration of NO2 in winter was higher than that in summer， and the average annual concentration was mainly between 4 μg/m3 and 28 μg/m3. TVOCs showed a unimodal trend and reached the peak in August， with an average concentration of 1954 μg/m3. Due to the favorable meteorological diffusion conditions， the concentrations of PM10 and PM2.5 had the same trend， and both had the highest concentration in December. 2） The high O3 concentration area was mainly concentrated in the north of the park； the spatiotemporal distribution characteristics of NO2 were significantly negatively correlated with O3， and the concentration of NO2 was lower in areas with high O3 concentration. There was no significant difference in the spatial distribution of TVOCs throughout the year， mainly concentrated in the south and northeast of the park； the areas with high PM values were mainly distributed in the middle of the park. 3） Influenced by the epidemic， the concentrations of NO2 and PM were lower than those before the control period， while the concentration of O3 was higher than that before the control period due to the influence of its precursors. 4） According to the Pearson correlation coefficient， the long-term migration of wind speed and high temperature would promote the formation of O3； according to the spatial distribution and the ratio of NO2 to TVOCs， the formation of O3 in the fine chemical industrial park was limited by NO2， and reducing the ambient NO2 concentration should be an effective strategy to reduce the O3 concentration in the park.
The chemical industry park has the characteristics of large scale， gathering enterprises， dense distribution， large quantity of dangerous chemicals， complex production process and so on. Based on the air pollutant monitoring network consists of 30 micro air quality monitoring stations， a long-term monitoring study of air pollutants in a fine chemical industrial park in Zhejiang province was carried out. The results showed that： 1） O3 concentration showed a bimodal change， and the peak appeared in April （83.8 μg/m3） and September （84.5 μg/m3）. The concentration of NO2 in winter was higher than that in summer， and the average annual concentration was mainly between 4 μg/m3 and 28 μg/m3. TVOCs showed a unimodal trend and reached the peak in August， with an average concentration of 1954 μg/m3. Due to the favorable meteorological diffusion conditions， the concentrations of PM10 and PM2.5 had the same trend， and both had the highest concentration in December. 2） The high O3 concentration area was mainly concentrated in the north of the park； the spatiotemporal distribution characteristics of NO2 were significantly negatively correlated with O3， and the concentration of NO2 was lower in areas with high O3 concentration. There was no significant difference in the spatial distribution of TVOCs throughout the year， mainly concentrated in the south and northeast of the park； the areas with high PM values were mainly distributed in the middle of the park. 3） Influenced by the epidemic， the concentrations of NO2 and PM were lower than those before the control period， while the concentration of O3 was higher than that before the control period due to the influence of its precursors. 4） According to the Pearson correlation coefficient， the long-term migration of wind speed and high temperature would promote the formation of O3； according to the spatial distribution and the ratio of NO2 to TVOCs， the formation of O3 in the fine chemical industrial park was limited by NO2， and reducing the ambient NO2 concentration should be an effective strategy to reduce the O3 concentration in the park.
2025,
43(4):
26-35.
doi: 10.13205/j.hjgc.202504003
Abstract:
In recent years， China's air quality has continued to improve， but the intensity and frequency of regional ozone （O3） pollution processes shows an increasing trend. It is of great significance to study the spatiotemporal variations of O3 precursors and their formation sensitivity in Jiangsu， Anhui， Shandong and Henan （Su-Wan-Lu-Yu Region）. This study utilized the Google Earth Engine （GEE） cloud platform and Sentinel-5P satellite remote sensing data to systematically analyze the spatiotemporal variations of ozone （O3） precursors and their sensitivity to O3 formation in the Su-Wan-Lu-Yu region from 2019 to 2023. The study found that the concentration of tropospheric formaldehyde （HCHO） column exhibited a distinct seasonal variation pattern of higher in summer and autumn， and lower in winter and spring， with the highest value in summer and an overall fluctuating increasing trend， particularly pronounced at the intersection of Henan， Anhui， and Shandong provinces. Conversely， the concentration of tropospheric nitrogen dioxide （NO2） column showed the characteristics of higher in autumn and winter， and lower in spring and summer， with the highest value in winter peaks and an overall fluctuating decreasing trend， indicating a significant reduction in nitrogen oxides （NOx ） emissions reduction effects in provincial capitals and surrounding cities. The O3 formation sensitivity to precursor emissions was predominantly influenced by synergistic control zones， followed by volatile organic compounds （VOCs） control areas， predominantly distributed around central cities such as Zhengzhou， Jinan， Xuzhou， Nanjing and Hefei， showing an increasing trend in coverage. The NOx control areas had the smallest coverage， mainly located in the western and southern non-central urban areas of Henan and Anhui provinces， showing an overall decreasing trend. This study provides scientific evidence for understanding the spatiotemporal distribution characteristics of O3 precursors in the Su-Wan-Lu-Yu region， but also provides a reference for the formulation of environmental management and pollution control policies.
In recent years， China's air quality has continued to improve， but the intensity and frequency of regional ozone （O3） pollution processes shows an increasing trend. It is of great significance to study the spatiotemporal variations of O3 precursors and their formation sensitivity in Jiangsu， Anhui， Shandong and Henan （Su-Wan-Lu-Yu Region）. This study utilized the Google Earth Engine （GEE） cloud platform and Sentinel-5P satellite remote sensing data to systematically analyze the spatiotemporal variations of ozone （O3） precursors and their sensitivity to O3 formation in the Su-Wan-Lu-Yu region from 2019 to 2023. The study found that the concentration of tropospheric formaldehyde （HCHO） column exhibited a distinct seasonal variation pattern of higher in summer and autumn， and lower in winter and spring， with the highest value in summer and an overall fluctuating increasing trend， particularly pronounced at the intersection of Henan， Anhui， and Shandong provinces. Conversely， the concentration of tropospheric nitrogen dioxide （NO2） column showed the characteristics of higher in autumn and winter， and lower in spring and summer， with the highest value in winter peaks and an overall fluctuating decreasing trend， indicating a significant reduction in nitrogen oxides （NOx ） emissions reduction effects in provincial capitals and surrounding cities. The O3 formation sensitivity to precursor emissions was predominantly influenced by synergistic control zones， followed by volatile organic compounds （VOCs） control areas， predominantly distributed around central cities such as Zhengzhou， Jinan， Xuzhou， Nanjing and Hefei， showing an increasing trend in coverage. The NOx control areas had the smallest coverage， mainly located in the western and southern non-central urban areas of Henan and Anhui provinces， showing an overall decreasing trend. This study provides scientific evidence for understanding the spatiotemporal distribution characteristics of O3 precursors in the Su-Wan-Lu-Yu region， but also provides a reference for the formulation of environmental management and pollution control policies.
2025,
43(4):
36-45.
doi: 10.13205/j.hjgc.202504004
Abstract:
Industry is an important engine of the national economy and a major source of carbon emissions. Research on industrial carbon emissions is crucial to achieving the "dual carbon" goal and regional sustainable development. The Pearl River Basin is a key area for economic and ecological environmental protection in China. However， there is no research on industrial carbon emissions in the Pearl River Basin. The industrial carbon emission efficiencies of 47 cities in the Pearl River Basin from 2009 to 2020 were evaluated using the super-efficiency SBM model. The spatial distribution characteristics and changes in local spatial relations of these cities' industrial carbon emission efficiencies were explored employing Moran's Index and LISA temporal transition methods. A geographical detector was used to identify the main influencing factors and their interactions， and a multi-scale geographically weighted regression model was applied to analyze the spatial heterogeneity of the influencing factors. The study found an overall rising trend in industrial carbon emission efficiency in the Pearl River Basin， with an average annual growth rate of 5.16%. Spatial distribution indicates that high-efficiency cities are concentrated in areas like Fangchenggang and Yuxi， while low-efficiency cities are primarily heavy industrial cities. The main factors affecting industrial carbon emission efficiency include productivity level，openness degree， and industrialization level. The influence of productivity level on industrial carbon emission efficiency diminished gradually in the eastern part of the basin， the degree of openness exhibits a negative impact on industrial carbon emission efficiency in most cities， and the impact of industrialization level varies， with higher values in developed regions and lower values in less-developed regions.
Industry is an important engine of the national economy and a major source of carbon emissions. Research on industrial carbon emissions is crucial to achieving the "dual carbon" goal and regional sustainable development. The Pearl River Basin is a key area for economic and ecological environmental protection in China. However， there is no research on industrial carbon emissions in the Pearl River Basin. The industrial carbon emission efficiencies of 47 cities in the Pearl River Basin from 2009 to 2020 were evaluated using the super-efficiency SBM model. The spatial distribution characteristics and changes in local spatial relations of these cities' industrial carbon emission efficiencies were explored employing Moran's Index and LISA temporal transition methods. A geographical detector was used to identify the main influencing factors and their interactions， and a multi-scale geographically weighted regression model was applied to analyze the spatial heterogeneity of the influencing factors. The study found an overall rising trend in industrial carbon emission efficiency in the Pearl River Basin， with an average annual growth rate of 5.16%. Spatial distribution indicates that high-efficiency cities are concentrated in areas like Fangchenggang and Yuxi， while low-efficiency cities are primarily heavy industrial cities. The main factors affecting industrial carbon emission efficiency include productivity level，openness degree， and industrialization level. The influence of productivity level on industrial carbon emission efficiency diminished gradually in the eastern part of the basin， the degree of openness exhibits a negative impact on industrial carbon emission efficiency in most cities， and the impact of industrialization level varies， with higher values in developed regions and lower values in less-developed regions.
2025,
43(4):
46-58.
doi: 10.13205/j.hjgc.202504005
Abstract:
As one of the important sources of global carbon emissions， the carbon emission prediction of the construction industry is crucial to promote the low-carbon transition and formulate effective carbon emission reduction policies. However， the existing carbon emission prediction models are limited in many aspects， especially in the accuracy of influencing factors selection， the integrity of data preprocessing， the complex dynamic changes of carbon emission data， the processing of nonlinear characteristics and so on. To solve these problems， this paper proposed a carbon emission prediction model for the construction industry based on maximum information coefficient （MIC） feature extraction， improved complementary ensemble empirical mode decomposition （ICEEMD）， RIME optimization algorithm， and deep hybrid kernel extreme learning machine （DHKELM）. First， according to the calculation method provided by the IPCC， this paper calculatesd the carbon emissions of China’s construction industry from 1992 to 2021， covering multiple factors of direct and indirect emissions. Combined with the STIRPAT model， 17 potential influencing factors were identified， including year-end total population， gross domestic product， construction area completed and energy mix. These factors fully reflected the driving mechanism of carbon emissions in the construction industry. Through grey correlation analysis and MIC method in two stages， this paper selected 12 key factors that had a significant impact on the carbon emissions of the construction industry. In the data preprocessing stage， the improved ICEEMD method was used to decompose the original carbon emission data， and several stationary sequences and one residual term were obtained. This decomposition process effectively reduced the noise in the data and enhanced the stationarity of the time series， thus providing more reliable data input for the subsequent modeling. Next， the RIME optimization algorithm was used to optimize the key parameters of the DHKELM model to further improve the model’s predictive power. DHKELM， as an effective deep learning algorithm， can adaptively capture the nonlinear characteristics of carbon emission data through the built-in hybrid kernel function. In the process of model construction and training， each decomposed sequence was input into the optimized DHKELM model for training and prediction. Finally， based on the forecast results of each decomposition sequence， the predicted value of carbon emissions of the construction industry as a whole was obtained. In order to evaluate the predictive performance of the model， the results of the model were compared with those of various benchmark models. The experimental results showed that the MIC-ICEEMD-RIME-DHKELM model was superior to other models in many performance indexes. Specifically， the model had a root mean square error of 0.2782 million tons， a mean absolute error of 0.2672 million tons， a mean absolute percentage error of 1.3783%， and an absolute correlation coefficient of 0.9576， showing its superior forecasting ability. Through the above analysis， the proposed MIC-ICEEMD-RIME-DHKELM model can effectively predict the carbon emission of the construction industry， and provide important theoretical support and practical reference for the carbon emission monitoring and policy formulation of the construction industry.
As one of the important sources of global carbon emissions， the carbon emission prediction of the construction industry is crucial to promote the low-carbon transition and formulate effective carbon emission reduction policies. However， the existing carbon emission prediction models are limited in many aspects， especially in the accuracy of influencing factors selection， the integrity of data preprocessing， the complex dynamic changes of carbon emission data， the processing of nonlinear characteristics and so on. To solve these problems， this paper proposed a carbon emission prediction model for the construction industry based on maximum information coefficient （MIC） feature extraction， improved complementary ensemble empirical mode decomposition （ICEEMD）， RIME optimization algorithm， and deep hybrid kernel extreme learning machine （DHKELM）. First， according to the calculation method provided by the IPCC， this paper calculatesd the carbon emissions of China’s construction industry from 1992 to 2021， covering multiple factors of direct and indirect emissions. Combined with the STIRPAT model， 17 potential influencing factors were identified， including year-end total population， gross domestic product， construction area completed and energy mix. These factors fully reflected the driving mechanism of carbon emissions in the construction industry. Through grey correlation analysis and MIC method in two stages， this paper selected 12 key factors that had a significant impact on the carbon emissions of the construction industry. In the data preprocessing stage， the improved ICEEMD method was used to decompose the original carbon emission data， and several stationary sequences and one residual term were obtained. This decomposition process effectively reduced the noise in the data and enhanced the stationarity of the time series， thus providing more reliable data input for the subsequent modeling. Next， the RIME optimization algorithm was used to optimize the key parameters of the DHKELM model to further improve the model’s predictive power. DHKELM， as an effective deep learning algorithm， can adaptively capture the nonlinear characteristics of carbon emission data through the built-in hybrid kernel function. In the process of model construction and training， each decomposed sequence was input into the optimized DHKELM model for training and prediction. Finally， based on the forecast results of each decomposition sequence， the predicted value of carbon emissions of the construction industry as a whole was obtained. In order to evaluate the predictive performance of the model， the results of the model were compared with those of various benchmark models. The experimental results showed that the MIC-ICEEMD-RIME-DHKELM model was superior to other models in many performance indexes. Specifically， the model had a root mean square error of 0.2782 million tons， a mean absolute error of 0.2672 million tons， a mean absolute percentage error of 1.3783%， and an absolute correlation coefficient of 0.9576， showing its superior forecasting ability. Through the above analysis， the proposed MIC-ICEEMD-RIME-DHKELM model can effectively predict the carbon emission of the construction industry， and provide important theoretical support and practical reference for the carbon emission monitoring and policy formulation of the construction industry.
2025,
43(4):
59-66.
doi: 10.13205/j.hjgc.202504006
Abstract:
China has made a major strategic decision to achieve the carbon peak and carbon neutrality goals， and consolidating and enhancing the ecosystem’s carbon sink capacity is an important action for realizing the goal. As a critical component of the global carbon cycle， the terrestrial ecosystem plays a huge role in the carbon sinks. The field investigation is a traditional method to monitor the carbon sink which has the problems of high labor cost and low work efficiency. In order to avoid and solve these problems effectively， this study took the core area of the Green Heart Central Park in the Changsha-Zhuzhou-Xiangtan urban agglomeration as the research object， and put forward a classification method of natural ecosystems， based on the classification framework of the Third National Land Resource Survey. By integrating satellite remote sensing imagery and some fundamental datasets， including precipitation， temperatures， and digital elevation models （DEM） and other basic data， a CASA_NEP model was constructed by combing the CASA model and the Net Ecosystem Productivity （NEP） approach， and this model was applied to estimate the NEP of the core area of the Changsha-Zhuzhou-Xiangtan Greenheart Central Park in 2022，which served as a quantitative indicator of vegetation carbon sink capacity of the core area. Furthermore， the spatial distribution and variations in vegetation carbon sinks across the different types of natural ecosystems in the core area were analyzed. The results indicated that the total annual vegetation carbon sink in the core area of the Changsha-Zhuzhou-Xiangtan Greenheart Central Park in 2022 reached 9471.51 Mg C/a， and the average carbon sink per unit area of the core area was 0.73 Mg C/（hm2·a）. Meanwhile， there was a significant difference of the vegetation carbon sink between the different ecosystem types in the core area， and forest ecosystem had the maximum of vegetation carbon sink， with a proportion of 98.28% of the total vegetation carbon sink of the core area， grassland ecosystem accounted for 1.64%， and wetland ecosystem accounted for only 0.08%. This method demonstrates high feasibility and operational efficiency which can offer substantial cost savings and improve the accuracy of carbon sink monitoring compared to traditional approaches. It can provide a scientific reference for large-scale vegetation carbon sink monitoring at a province level and even the country level.
China has made a major strategic decision to achieve the carbon peak and carbon neutrality goals， and consolidating and enhancing the ecosystem’s carbon sink capacity is an important action for realizing the goal. As a critical component of the global carbon cycle， the terrestrial ecosystem plays a huge role in the carbon sinks. The field investigation is a traditional method to monitor the carbon sink which has the problems of high labor cost and low work efficiency. In order to avoid and solve these problems effectively， this study took the core area of the Green Heart Central Park in the Changsha-Zhuzhou-Xiangtan urban agglomeration as the research object， and put forward a classification method of natural ecosystems， based on the classification framework of the Third National Land Resource Survey. By integrating satellite remote sensing imagery and some fundamental datasets， including precipitation， temperatures， and digital elevation models （DEM） and other basic data， a CASA_NEP model was constructed by combing the CASA model and the Net Ecosystem Productivity （NEP） approach， and this model was applied to estimate the NEP of the core area of the Changsha-Zhuzhou-Xiangtan Greenheart Central Park in 2022，which served as a quantitative indicator of vegetation carbon sink capacity of the core area. Furthermore， the spatial distribution and variations in vegetation carbon sinks across the different types of natural ecosystems in the core area were analyzed. The results indicated that the total annual vegetation carbon sink in the core area of the Changsha-Zhuzhou-Xiangtan Greenheart Central Park in 2022 reached 9471.51 Mg C/a， and the average carbon sink per unit area of the core area was 0.73 Mg C/（hm2·a）. Meanwhile， there was a significant difference of the vegetation carbon sink between the different ecosystem types in the core area， and forest ecosystem had the maximum of vegetation carbon sink， with a proportion of 98.28% of the total vegetation carbon sink of the core area， grassland ecosystem accounted for 1.64%， and wetland ecosystem accounted for only 0.08%. This method demonstrates high feasibility and operational efficiency which can offer substantial cost savings and improve the accuracy of carbon sink monitoring compared to traditional approaches. It can provide a scientific reference for large-scale vegetation carbon sink monitoring at a province level and even the country level.
2025,
43(4):
67-77.
doi: 10.13205/j.hjgc.202504007
Abstract:
With the advancement of industrialization and urbanization， the illegal use of contaminated land has become increasingly serious. There is an urgent need to carry out identification and monitoring research on the use of contaminated land to meet the requirements of environmental management and resource protection. In view of the technical challenge of difficult identification and monitoring of land-use activities in contaminated plots， a technical system for identifying and monitoring of such activities was established， based on multi-modal data such as remote sensing images and monitoring data. Taking the contaminated plots in the Chongqing Construction Land Soil Pollution Risk Management and Remediation List as an example， multi-modal data were used to identify and monitor 70 contaminated plots， achieving an overall classification accuracy of 96%. On the whole， planting activities dominated. There were 23 contaminated plots with land-use activities， among which 18 plots had planting activities， accounting for 78.26%. The number of land-use activities， land-use activity points， and land-use activity areas in contaminated plots varied greatly among districts and counties. Spatially， the land-use activities of contaminated land exhibited a clustered distribution. The high-density core area was located in Shapingba District， and the higher-density core areas were located in Dadukou District and Jiulongpo District， showing an obvious "core-to-edge" structure. The research results can help formulate scientific soil risk management and control measures， develop protection and restoration plans， and reduce the potential risks of soil pollution to the environment and human health.
With the advancement of industrialization and urbanization， the illegal use of contaminated land has become increasingly serious. There is an urgent need to carry out identification and monitoring research on the use of contaminated land to meet the requirements of environmental management and resource protection. In view of the technical challenge of difficult identification and monitoring of land-use activities in contaminated plots， a technical system for identifying and monitoring of such activities was established， based on multi-modal data such as remote sensing images and monitoring data. Taking the contaminated plots in the Chongqing Construction Land Soil Pollution Risk Management and Remediation List as an example， multi-modal data were used to identify and monitor 70 contaminated plots， achieving an overall classification accuracy of 96%. On the whole， planting activities dominated. There were 23 contaminated plots with land-use activities， among which 18 plots had planting activities， accounting for 78.26%. The number of land-use activities， land-use activity points， and land-use activity areas in contaminated plots varied greatly among districts and counties. Spatially， the land-use activities of contaminated land exhibited a clustered distribution. The high-density core area was located in Shapingba District， and the higher-density core areas were located in Dadukou District and Jiulongpo District， showing an obvious "core-to-edge" structure. The research results can help formulate scientific soil risk management and control measures， develop protection and restoration plans， and reduce the potential risks of soil pollution to the environment and human health.
2025,
43(4):
78-87.
doi: 10.13205/j.hjgc.202504008
Abstract:
In order to explore the heavy metal pollution status of soil in the leftover contaminated plots of an iron and steel plant area relocation in Chongqing， 1427 samples were collected from 262 sampling points based on gridding and professional judgment， and the mean values of the indexes were taken as the values of the sampling points.The current status of soil pollution in the study area was evaluated using the single factor pollution index and the Nemero comprehensive pollution index， and the spatial differentiation of soil pollution influencing factors was analyzed by using geo-detectors. The results showed that：1） the average values of As， Cd， Cu， Hg， Ni， and Pb were 8.18， 0.47， 49.46， 0.24， 29.37， 63.98， 8.98 mg/kg， respectively， and all of them exceeded the standard compared with the background values except for Ni， with strong variability； the spatial distribution varied greatly， and the enriched areas were distributed along the areas with high incidence of industrial utilization. 2）the mean value of soil heavy metal Nemero comprehensive pollution index （P） amounted to 5.46， which was heavily polluted， and the plots were seriously polluted， and the P values of heavy metals in the central part of the study area was larger than that in the eastern and western parts. 3）the factor detection showed that distance from the water source had the strongest explanatory power for pollution distribution （PD，H=0.14） and slope had the weakest explanatory power （PD，H=0.01）. The interaction detection showed that the distribution of heavy metal pollution in the study area was influenced by a combination of factors， with the combination of distance from the water source ∩ pH （PD，H=0.28） having the strongest influence， and the combination of distance from road ∩ slope （PD，H=0.05） having the least influence.The study showed that there was variability in heavy metal enrichment degree in polluted sites， which was mainly affected by various factors such as geographic location and industrial activities， and provided an important basis for subsequent pollution management.
In order to explore the heavy metal pollution status of soil in the leftover contaminated plots of an iron and steel plant area relocation in Chongqing， 1427 samples were collected from 262 sampling points based on gridding and professional judgment， and the mean values of the indexes were taken as the values of the sampling points.The current status of soil pollution in the study area was evaluated using the single factor pollution index and the Nemero comprehensive pollution index， and the spatial differentiation of soil pollution influencing factors was analyzed by using geo-detectors. The results showed that：1） the average values of As， Cd， Cu， Hg， Ni， and Pb were 8.18， 0.47， 49.46， 0.24， 29.37， 63.98， 8.98 mg/kg， respectively， and all of them exceeded the standard compared with the background values except for Ni， with strong variability； the spatial distribution varied greatly， and the enriched areas were distributed along the areas with high incidence of industrial utilization. 2）the mean value of soil heavy metal Nemero comprehensive pollution index （P） amounted to 5.46， which was heavily polluted， and the plots were seriously polluted， and the P values of heavy metals in the central part of the study area was larger than that in the eastern and western parts. 3）the factor detection showed that distance from the water source had the strongest explanatory power for pollution distribution （PD，H=0.14） and slope had the weakest explanatory power （PD，H=0.01）. The interaction detection showed that the distribution of heavy metal pollution in the study area was influenced by a combination of factors， with the combination of distance from the water source ∩ pH （PD，H=0.28） having the strongest influence， and the combination of distance from road ∩ slope （PD，H=0.05） having the least influence.The study showed that there was variability in heavy metal enrichment degree in polluted sites， which was mainly affected by various factors such as geographic location and industrial activities， and provided an important basis for subsequent pollution management.
2025,
43(4):
88-97.
doi: 10.13205/j.hjgc.202504009
Abstract:
In view of the ubiquitous and potential hazards of per- and polyfluoroalkyl substances （PFAS） in the environment， the paper classified and summarized the application research progress of molecular imprinting sensors for detecting PFAS， and explored the detection principle， performance and signal conversion mechanism of the sensors. Traditional chromatography and mass spectrometry are expensive and complicated in detecting PFAS， and require professional operators， cannot meet the requirements of in-situ detection and continuous monitoring. Previous studies have shown that molecular imprinting sensors can respond quickly to the target analyte， reduce the detection complexity and cost of PFAS， and is a promising method for the determination of PFAS. Although the molecularly imprinting sensor has made great progress in application of PFAS detection， it still faces challenges in practical application， such as sensitivity， selectivity， portability and commercialization， which need further improvement. Future study should focus on the application of nano-materials and microelectrodes in the detection of molecularly imprinting sensors， the research of on-site continuous monitoring sensors， as well as the standardization， miniaturization and intelligence of molecular imprinting sensors.
In view of the ubiquitous and potential hazards of per- and polyfluoroalkyl substances （PFAS） in the environment， the paper classified and summarized the application research progress of molecular imprinting sensors for detecting PFAS， and explored the detection principle， performance and signal conversion mechanism of the sensors. Traditional chromatography and mass spectrometry are expensive and complicated in detecting PFAS， and require professional operators， cannot meet the requirements of in-situ detection and continuous monitoring. Previous studies have shown that molecular imprinting sensors can respond quickly to the target analyte， reduce the detection complexity and cost of PFAS， and is a promising method for the determination of PFAS. Although the molecularly imprinting sensor has made great progress in application of PFAS detection， it still faces challenges in practical application， such as sensitivity， selectivity， portability and commercialization， which need further improvement. Future study should focus on the application of nano-materials and microelectrodes in the detection of molecularly imprinting sensors， the research of on-site continuous monitoring sensors， as well as the standardization， miniaturization and intelligence of molecular imprinting sensors.
2025,
43(4):
98-109.
doi: 10.13205/j.hjgc.202504010
Abstract:
In recent years， the rapid development of artificial intelligence technology has attracted the attention of researchers in most fields， and fruitful achievements have been made in agriculture， climate， security， and the environment. Firstly， the review summarized the basic principles， technical characteristics， and application scope of artificial intelligence technologies such as fuzzy logic， genetic algorithms， artificial neural networks， XGBoost， LightGBM， etc. Moreover， it focused on the technical advancements and advantages of hybrid intelligent systems which combined artificial neural networks and different artificial intelligence technologies. Secondly， taking the field of atmospheric environment as an example， the application progress of artificial intelligence technology in environmental fields was investigated. Especially， by summarizing the common input parameter types and performance evaluation indexes for the prediction of different air pollutants， the prediction applications in the field of atmospheric environment of single artificial intelligence models and hybrid artificial intelligence models were illustrated with specific cases. Generally， artificial model prediction accuracy is affected by various factors， including the input parameters and model types. Compared to single neural network models， hybrid intelligent models have a relatively higher predictive performance. Finally， the challenges of artificial intelligence in the atmospheric environment field were analyzed from environmental element data sets， model training methods， and algorithm defects. In the future， the applications of artificial intelligence technology in the atmospheric environment field should be improved from the following aspects： hybrid intelligent system combinations， comprehensive analysis of complicated environmental elements， and cooperative working with environmental protection platforms.
In recent years， the rapid development of artificial intelligence technology has attracted the attention of researchers in most fields， and fruitful achievements have been made in agriculture， climate， security， and the environment. Firstly， the review summarized the basic principles， technical characteristics， and application scope of artificial intelligence technologies such as fuzzy logic， genetic algorithms， artificial neural networks， XGBoost， LightGBM， etc. Moreover， it focused on the technical advancements and advantages of hybrid intelligent systems which combined artificial neural networks and different artificial intelligence technologies. Secondly， taking the field of atmospheric environment as an example， the application progress of artificial intelligence technology in environmental fields was investigated. Especially， by summarizing the common input parameter types and performance evaluation indexes for the prediction of different air pollutants， the prediction applications in the field of atmospheric environment of single artificial intelligence models and hybrid artificial intelligence models were illustrated with specific cases. Generally， artificial model prediction accuracy is affected by various factors， including the input parameters and model types. Compared to single neural network models， hybrid intelligent models have a relatively higher predictive performance. Finally， the challenges of artificial intelligence in the atmospheric environment field were analyzed from environmental element data sets， model training methods， and algorithm defects. In the future， the applications of artificial intelligence technology in the atmospheric environment field should be improved from the following aspects： hybrid intelligent system combinations， comprehensive analysis of complicated environmental elements， and cooperative working with environmental protection platforms.
2025,
43(4):
110-120.
doi: 10.13205/j.hjgc.202504011
Abstract:
To address the problems of the low accuracy for dense targets and are easily interfered by the environmental background in current garbage detection algorithms， this paper proposed a garbage detection algorithm YOLOv8-MA based on the improvement of YOLOv8. The method integrated the GAM （Global Attention Mechanism） and the MLCA （Mixed Local Channel Attention） mechanism into the YOLOv8 backbone network to enhance the capacity to capture regional context information. The GAM global attention mechanism enhanced the model's capacity to perceive and utilize global information by reducing information loss and amplifying global interactions. The MLCA mixed local channel attention mechanism focused more precisely on local feature representations， which improved the model's detection capacity to distinguish stacked garbage under complex backgrounds for achieving a significant improvement in the detection accuracy of garbage classification detection tasks， perform global attention on the input feature map and pay more attention to pixel-level information in higher-level feature maps， effectively capture the cross-dimensional interactions and establish the dependency relations between dimensions to achieve a significant improvement in detection accuracy. In addition， the algorithm adopted the EIoU loss function to enhance the adaptability of the model to adapt to various samples by optimizing the regression process of the bounding box， accelerating the convergence speed of the model for locating garbage targets， enhancing the model's detection capacity to distinguish stacked garbage under complex backgrounds. The experiments conducted on TACO datasets had achieved good results， the results showed that compared with the baseline model YOLOv8， YOLOv8-MA had improved the precision， recall， mAP@0.5 and mAP@0.5∶0.95 evaluation indicators by 1.4%， 5.3%， 4.2% and 5.4%， respectively， showing an outstanding performance and having a better detection effect. The experimental results proved the effectiveness and excellence of the YOLOv8-MA algorithm. In addition， the experiment of ablation proved the effectiveness of each improvement module in enhancing the model performance.
To address the problems of the low accuracy for dense targets and are easily interfered by the environmental background in current garbage detection algorithms， this paper proposed a garbage detection algorithm YOLOv8-MA based on the improvement of YOLOv8. The method integrated the GAM （Global Attention Mechanism） and the MLCA （Mixed Local Channel Attention） mechanism into the YOLOv8 backbone network to enhance the capacity to capture regional context information. The GAM global attention mechanism enhanced the model's capacity to perceive and utilize global information by reducing information loss and amplifying global interactions. The MLCA mixed local channel attention mechanism focused more precisely on local feature representations， which improved the model's detection capacity to distinguish stacked garbage under complex backgrounds for achieving a significant improvement in the detection accuracy of garbage classification detection tasks， perform global attention on the input feature map and pay more attention to pixel-level information in higher-level feature maps， effectively capture the cross-dimensional interactions and establish the dependency relations between dimensions to achieve a significant improvement in detection accuracy. In addition， the algorithm adopted the EIoU loss function to enhance the adaptability of the model to adapt to various samples by optimizing the regression process of the bounding box， accelerating the convergence speed of the model for locating garbage targets， enhancing the model's detection capacity to distinguish stacked garbage under complex backgrounds. The experiments conducted on TACO datasets had achieved good results， the results showed that compared with the baseline model YOLOv8， YOLOv8-MA had improved the precision， recall， mAP@0.5 and mAP@0.5∶0.95 evaluation indicators by 1.4%， 5.3%， 4.2% and 5.4%， respectively， showing an outstanding performance and having a better detection effect. The experimental results proved the effectiveness and excellence of the YOLOv8-MA algorithm. In addition， the experiment of ablation proved the effectiveness of each improvement module in enhancing the model performance.
2025,
43(4):
121-131.
doi: 10.13205/j.hjgc.202504012
Abstract:
The application of machine learning in the water quality monitoring of wastewater treatment processes has become a significant focus of contemporary research. Addressing the challenges about time lag and high detection cost in acquiring key water quality indicators of traditional monitoring methods in wastewater treatment processes， this study focused on the prediction of nitrite nitrogen （NO2--N） concentration in the shortcut nitrification-denitrification process， proposed a novel soft sensing method for water quality indicators based on the decision tree integration model. This study selected some easily measured and important operating parameters as the input features of the water quality soft sensing method， constructed four categories of tree integration models based on the decision tree to predict the effluent NO2--N concentration accurately in the denitrification process. The prediction accuracy and stability of each model were compared to select the best-performing one. The importance level of these models’ input features to the NO2--N prediction results was analyzed to interpret the optimal prediction model. Moreover， the study utilized feature selection analysis to further validate the interpretability of these tree integration models’ results. The research results indicated that among these four decision tree integration models， the Adaptive Boosting （AdaBoost） model exhibited the highest predictive accuracy and stability for the effluent NO2--N concentration in the denitrification process. In the prediction performance of the AdaBoost model， the determination coefficient （R2）， mean square error （MSE） and mean absolute percentage error （MAPE） was 0.983， 0.015 and 0.126， better than other models. The results of model interpretation revealed that pH， oxidation reduction potential （ORP） and influent chemical oxygen demand （COD） concentration were the most critical parameters for these decision tree integration models. These parameters affected models’ prediction results significantly and exhibited strong correlations with the effluent NO2--N concentration in the denitrification process. Further feature selection analysis confirmed that these parameters play crucial roles in improving the prediction accuracy of these models. In this study， the soft sensing method based on decision tree integration models provided a valuable reference for achieving low-cost and real-time prediction of water quality indicators， expanding the availability of effective data to enhancing prediction accuracy for all kinds of prediction models in wastewater treatment processes.
The application of machine learning in the water quality monitoring of wastewater treatment processes has become a significant focus of contemporary research. Addressing the challenges about time lag and high detection cost in acquiring key water quality indicators of traditional monitoring methods in wastewater treatment processes， this study focused on the prediction of nitrite nitrogen （NO2--N） concentration in the shortcut nitrification-denitrification process， proposed a novel soft sensing method for water quality indicators based on the decision tree integration model. This study selected some easily measured and important operating parameters as the input features of the water quality soft sensing method， constructed four categories of tree integration models based on the decision tree to predict the effluent NO2--N concentration accurately in the denitrification process. The prediction accuracy and stability of each model were compared to select the best-performing one. The importance level of these models’ input features to the NO2--N prediction results was analyzed to interpret the optimal prediction model. Moreover， the study utilized feature selection analysis to further validate the interpretability of these tree integration models’ results. The research results indicated that among these four decision tree integration models， the Adaptive Boosting （AdaBoost） model exhibited the highest predictive accuracy and stability for the effluent NO2--N concentration in the denitrification process. In the prediction performance of the AdaBoost model， the determination coefficient （R2）， mean square error （MSE） and mean absolute percentage error （MAPE） was 0.983， 0.015 and 0.126， better than other models. The results of model interpretation revealed that pH， oxidation reduction potential （ORP） and influent chemical oxygen demand （COD） concentration were the most critical parameters for these decision tree integration models. These parameters affected models’ prediction results significantly and exhibited strong correlations with the effluent NO2--N concentration in the denitrification process. Further feature selection analysis confirmed that these parameters play crucial roles in improving the prediction accuracy of these models. In this study， the soft sensing method based on decision tree integration models provided a valuable reference for achieving low-cost and real-time prediction of water quality indicators， expanding the availability of effective data to enhancing prediction accuracy for all kinds of prediction models in wastewater treatment processes.
2025,
43(4):
132-142.
doi: 10.13205/j.hjgc.202504013
Abstract:
With the rapid economic development and accelerated urbanization， the urban water consumption population in China has been continuously increasing. While traditional water treatment processes ensure the safety of drinking water， they also generate tens of millions of cubic meters of drinking water treatment residues （DWTR） annually. Compared to wastewater treatment sludge， the disposal of DWTR has received relatively less attention in research and practice. Therefore， the environmentally friendly treatment and resource utilization of DWTR deserve greater attention. This paper aims to review recent advances in preparation methods of DWTR-based functional materials， analyzes the influence of these methods on their structure and adsorption properties， identifies the sources and mechanisms of their primary adsorption active sites， and discusses the limitations and challenges faced by DWTR-based functional materials for pollutant adsorption. The findings indicated that the inherent inorganic substances， such as Al and Fe， in DWTR not only served as active sites for pollutant adsorption but also provided structural frameworks for the synthesis of composite functional materials， thereby enhancing pollutant adsorption. While techniques such as pyrolysis， activation， and compounding could improve the adsorption performance of DWTR-based functional materials， they also led to increased cost， presenting a big challenge for widespread application. Therefore， this paper proposes to provide guidance for the preparation of DWTR-based functional materials and the optimization of their adsorption properties through life cycle assessment and cost-benefit analysis， integrated with machine learning simulations. Furthermore， future research should aim to expand the scope of targeted pollutants or focus on the high-value recycling of DWTR resources， thereby achieving low-carbon recycling and sustainable utilization of DWTR-based materials in the context of the “Dual Carbon” goals.
With the rapid economic development and accelerated urbanization， the urban water consumption population in China has been continuously increasing. While traditional water treatment processes ensure the safety of drinking water， they also generate tens of millions of cubic meters of drinking water treatment residues （DWTR） annually. Compared to wastewater treatment sludge， the disposal of DWTR has received relatively less attention in research and practice. Therefore， the environmentally friendly treatment and resource utilization of DWTR deserve greater attention. This paper aims to review recent advances in preparation methods of DWTR-based functional materials， analyzes the influence of these methods on their structure and adsorption properties， identifies the sources and mechanisms of their primary adsorption active sites， and discusses the limitations and challenges faced by DWTR-based functional materials for pollutant adsorption. The findings indicated that the inherent inorganic substances， such as Al and Fe， in DWTR not only served as active sites for pollutant adsorption but also provided structural frameworks for the synthesis of composite functional materials， thereby enhancing pollutant adsorption. While techniques such as pyrolysis， activation， and compounding could improve the adsorption performance of DWTR-based functional materials， they also led to increased cost， presenting a big challenge for widespread application. Therefore， this paper proposes to provide guidance for the preparation of DWTR-based functional materials and the optimization of their adsorption properties through life cycle assessment and cost-benefit analysis， integrated with machine learning simulations. Furthermore， future research should aim to expand the scope of targeted pollutants or focus on the high-value recycling of DWTR resources， thereby achieving low-carbon recycling and sustainable utilization of DWTR-based materials in the context of the “Dual Carbon” goals.
2025,
43(4):
143-155.
doi: 10.13205/j.hjgc.202504014
Abstract:
At present，the common types of toilets in rural areas of China are generally characterized by the backword waste treatment technology，poor resource utilization and insufficient water-saving，which constrains the promotion of the "toilet revolution" in rural areas. In this paper，based on the fecal-urine segregation-type flush toilet，the filler adsorption coupled with soil filtration was applied to treat the yellow water in the toilet，rice husk biochar and zeolite were chosen as the fillers of the adsorption system， and zeolite， coal ash residue， corn cob，iron particles and garden soil were used as the fillers of the soil filtration system. The experimental parameters such as hydraulic load，toilet flushing water volume and wet/dry ratio were designed to investigate the adsorption effects of nitrogen and phosphorus in the yellow water and the purification capacity. The better parameters were screened out and selected，and a long-term operation was carried out to evaluate the stability of the system operation and the replacement cycle of the test filler. The results showed that with the parameters of hydraulic load of 0.34 m3/（m2·d），toilet flushing volume of 3 L，and wet/dry ratio of 2∶1，the treatment system had the best effect on nutrient recovery and pollutant purification of yellow water. Under these conditions，the system operated stably for 98 d without any blockage. The average removal rates of TN and TP in the total effluent were both higher than 94%，and the average concentration of COD was 21.57 mg/L，which met the requirements of ISO 30500 effluent standard. According to the test parameters，based on the applications in rural single-family households，the key components were parameterized and integrated to carry out the overall design of the toilet，which finally formed a new type of water-recirculating flush toilets. This study provides an important reference for the local resource utilization of yellow water and resource recycling water-saving toilet technology in rural areas of China.
At present，the common types of toilets in rural areas of China are generally characterized by the backword waste treatment technology，poor resource utilization and insufficient water-saving，which constrains the promotion of the "toilet revolution" in rural areas. In this paper，based on the fecal-urine segregation-type flush toilet，the filler adsorption coupled with soil filtration was applied to treat the yellow water in the toilet，rice husk biochar and zeolite were chosen as the fillers of the adsorption system， and zeolite， coal ash residue， corn cob，iron particles and garden soil were used as the fillers of the soil filtration system. The experimental parameters such as hydraulic load，toilet flushing water volume and wet/dry ratio were designed to investigate the adsorption effects of nitrogen and phosphorus in the yellow water and the purification capacity. The better parameters were screened out and selected，and a long-term operation was carried out to evaluate the stability of the system operation and the replacement cycle of the test filler. The results showed that with the parameters of hydraulic load of 0.34 m3/（m2·d），toilet flushing volume of 3 L，and wet/dry ratio of 2∶1，the treatment system had the best effect on nutrient recovery and pollutant purification of yellow water. Under these conditions，the system operated stably for 98 d without any blockage. The average removal rates of TN and TP in the total effluent were both higher than 94%，and the average concentration of COD was 21.57 mg/L，which met the requirements of ISO 30500 effluent standard. According to the test parameters，based on the applications in rural single-family households，the key components were parameterized and integrated to carry out the overall design of the toilet，which finally formed a new type of water-recirculating flush toilets. This study provides an important reference for the local resource utilization of yellow water and resource recycling water-saving toilet technology in rural areas of China.
2025,
43(4):
156-164.
doi: 10.13205/j.hjgc.202504015
Abstract:
The treatment technologies for waste emulsions are diverse， with significant differences in technical characteristics and applicability. However， a unified standard for technology evaluation has not yet been established. Under the backdrop of carbon peaking and carbon neutrality， how to conduct a comprehensive and objective technical evaluation of waste emulsion treatment technologies has attracted great attention. In this paper， a comprehensive evaluation system of Water-Energy-Carbon was constructed， and the waste emulsion treatment technologies such as chemical demulsification， evaporation， and membrane separation were comprehensively evaluated by using influent water quality， oil reduction， electricity consumption， energy consumption carbon emissions， drug consumption carbon emissions， carbon compensation indexes， and Water-Energy-Carbon coupling index （WECCI）， and these technologies were quantitatively analyzed in combination with engineering cases.The research revealed that the traditional chemical demulsification method was the most widely applied， primarily for waste emulsions with simple compositions and low pollutants concentrations. It was characterized by low energy consumption but high chemical consumption， with an energy consumption typically below 10 kW·h/t and carbon emissions ranging from 20 to 80 kg CO2eq/kg. Evaporation methods were also employed for waste emulsion treatment in recent years， particularly for complex， stable emulsions with high oil content. It was characterized by high energy consumption and high carbon emissions， with energy consumption reaching 60 kW·h/t and carbon emissions ranging from 10 to 40 kg CO2eq/kg.Membrane separation technology was extensively studied experimentally. It was characterized by low energy consumption and low carbon emissions， with energy consumption typically ranging from 5 kW·h/t to 35 kW·h/t and carbon emissions below 20 kg CO2eq/kg. This method showed promising prospects for future development. The Water-Energy-Carbon evaluation results of a waste emulsion treatment center in Jiangsu showed that the carbon emissions of mechanical vapor recompression （MVR）， coagulation air flotation， and magnetic flocculation units were 38.55， 22.87 and 13.39 kg CO2eq/kg， respectively， and their WECCI were 0.379， 0.524， and 0.705， respectively. MVR had strong universality and good treatment effect， but the energy consumption and carbon emissions were high， and the comprehensive performance was medididare. As a new type of chemical demulsification method， magnetic flocculation had good treatment effects， low energy consumption and carbon emissions， high level of hydro-carbon coupling， indicating a better comprehensive performance. This study provides a new multidimensional method for the evaluation and selection of waste emulsion treatment technologies. Future research can expand the evaluation system by incorporating indicators such as lifecycle assessment and regional adaptability， ensuring its high applicability in diverse environments.
The treatment technologies for waste emulsions are diverse， with significant differences in technical characteristics and applicability. However， a unified standard for technology evaluation has not yet been established. Under the backdrop of carbon peaking and carbon neutrality， how to conduct a comprehensive and objective technical evaluation of waste emulsion treatment technologies has attracted great attention. In this paper， a comprehensive evaluation system of Water-Energy-Carbon was constructed， and the waste emulsion treatment technologies such as chemical demulsification， evaporation， and membrane separation were comprehensively evaluated by using influent water quality， oil reduction， electricity consumption， energy consumption carbon emissions， drug consumption carbon emissions， carbon compensation indexes， and Water-Energy-Carbon coupling index （WECCI）， and these technologies were quantitatively analyzed in combination with engineering cases.The research revealed that the traditional chemical demulsification method was the most widely applied， primarily for waste emulsions with simple compositions and low pollutants concentrations. It was characterized by low energy consumption but high chemical consumption， with an energy consumption typically below 10 kW·h/t and carbon emissions ranging from 20 to 80 kg CO2eq/kg. Evaporation methods were also employed for waste emulsion treatment in recent years， particularly for complex， stable emulsions with high oil content. It was characterized by high energy consumption and high carbon emissions， with energy consumption reaching 60 kW·h/t and carbon emissions ranging from 10 to 40 kg CO2eq/kg.Membrane separation technology was extensively studied experimentally. It was characterized by low energy consumption and low carbon emissions， with energy consumption typically ranging from 5 kW·h/t to 35 kW·h/t and carbon emissions below 20 kg CO2eq/kg. This method showed promising prospects for future development. The Water-Energy-Carbon evaluation results of a waste emulsion treatment center in Jiangsu showed that the carbon emissions of mechanical vapor recompression （MVR）， coagulation air flotation， and magnetic flocculation units were 38.55， 22.87 and 13.39 kg CO2eq/kg， respectively， and their WECCI were 0.379， 0.524， and 0.705， respectively. MVR had strong universality and good treatment effect， but the energy consumption and carbon emissions were high， and the comprehensive performance was medididare. As a new type of chemical demulsification method， magnetic flocculation had good treatment effects， low energy consumption and carbon emissions， high level of hydro-carbon coupling， indicating a better comprehensive performance. This study provides a new multidimensional method for the evaluation and selection of waste emulsion treatment technologies. Future research can expand the evaluation system by incorporating indicators such as lifecycle assessment and regional adaptability， ensuring its high applicability in diverse environments.
2025,
43(4):
165-173.
doi: 10.13205/j.hjgc.202504016
Abstract:
The study investigated the performance of an intermittent aeration sequencing batch reactors （IASBR） in low dissolved oxygen conditions （approximately 0.5 mg/L）. Degradation of organic compounds and nitrogen removal through the partial nitrification and denitrification were studied for treating of wastewater with different initial aniline concentrations （5， 20 and 30 mg/L）. The evolution of microbial communities were also analyzed based on high-throughput sequencing data. The experimental results indicated that when aniline concentration was from 5 mg/L to 30 mg/L， the IASBRs demonstrated excellent performance in removal of COD and aniline. The average removal rate of COD exceeded 80%. The removal rate of aniline was 85% above on average when the reactors gradually stabilized in the later stages. When the concentrations of aniline were 5 mg/L and 20 mg/L， the average NH4+-N removal rates were 98.01% and 93.03%， the average total inorganic nitrogen removal rates were 83.79% and 79.74%， and the average accumulation rates of nitrite were 86.58% and 95.57%， respectively. The performance of partial nitrification and denitrification could effectively achieve nitrogen removal. However， when the aniline concentration increased to 30 mg/L， the average removal rates of NH4+-N and total inorganic nitrogen decreased to 50.57% and 40.48%， respectively， because higher concentrations of aniline inhibited the activity of nitrobacteria and denitrifying bacteria. High-throughput sequencing results revealed a slight decrease in microbial diversity of sludge after adding aniline. The dominant phyla in test group C0 （inoculated sludge）， C1 （5 mg/L）， and C2 （20 mg/L） were Bacteroidetes， Proteobacteria and Candidatus_Saccharibacteria. With increasing influent aniline concentration， certain bacterial genera capable of degrading aromatic amine compounds were enriched， such as unclassified_Sphingobacteriales. However， bacterial genera involved in nitrogen metabolism were inhibited， such as unclassified_Xanthomonadaceae. Additionally， AOB （ammonia oxidizing bacteria） had a significant advantage over NOB （nitrite oxidizing bacteria） in three sludge samples， indicating that AOB had stronger tolerance to aniline. It was the main reason for achieving partial nitrification denitrification in the system. Metabolic predictions based on PICRUSt software and annotation analysis using the KEGG database indicated that the microbial metabolism of aniline in the system mainly involved ortho-metabolism and meta-metabolism processes.
The study investigated the performance of an intermittent aeration sequencing batch reactors （IASBR） in low dissolved oxygen conditions （approximately 0.5 mg/L）. Degradation of organic compounds and nitrogen removal through the partial nitrification and denitrification were studied for treating of wastewater with different initial aniline concentrations （5， 20 and 30 mg/L）. The evolution of microbial communities were also analyzed based on high-throughput sequencing data. The experimental results indicated that when aniline concentration was from 5 mg/L to 30 mg/L， the IASBRs demonstrated excellent performance in removal of COD and aniline. The average removal rate of COD exceeded 80%. The removal rate of aniline was 85% above on average when the reactors gradually stabilized in the later stages. When the concentrations of aniline were 5 mg/L and 20 mg/L， the average NH4+-N removal rates were 98.01% and 93.03%， the average total inorganic nitrogen removal rates were 83.79% and 79.74%， and the average accumulation rates of nitrite were 86.58% and 95.57%， respectively. The performance of partial nitrification and denitrification could effectively achieve nitrogen removal. However， when the aniline concentration increased to 30 mg/L， the average removal rates of NH4+-N and total inorganic nitrogen decreased to 50.57% and 40.48%， respectively， because higher concentrations of aniline inhibited the activity of nitrobacteria and denitrifying bacteria. High-throughput sequencing results revealed a slight decrease in microbial diversity of sludge after adding aniline. The dominant phyla in test group C0 （inoculated sludge）， C1 （5 mg/L）， and C2 （20 mg/L） were Bacteroidetes， Proteobacteria and Candidatus_Saccharibacteria. With increasing influent aniline concentration， certain bacterial genera capable of degrading aromatic amine compounds were enriched， such as unclassified_Sphingobacteriales. However， bacterial genera involved in nitrogen metabolism were inhibited， such as unclassified_Xanthomonadaceae. Additionally， AOB （ammonia oxidizing bacteria） had a significant advantage over NOB （nitrite oxidizing bacteria） in three sludge samples， indicating that AOB had stronger tolerance to aniline. It was the main reason for achieving partial nitrification denitrification in the system. Metabolic predictions based on PICRUSt software and annotation analysis using the KEGG database indicated that the microbial metabolism of aniline in the system mainly involved ortho-metabolism and meta-metabolism processes.
2025,
43(4):
174-181.
doi: 10.13205/j.hjgc.202504017
Abstract:
The treatment of coal chemical wastewater has become a significant challenge， restricting the development of coal chemical enterprises. While the dual-membrane process of "nanofiltration + reverse osmosis" is commonly employed for separating organic substances and inorganic salts in wastewater， it's insufficient in treating the reverse osmosis brine generated during this process. This study focused on exploring a more efficient method to treat the reverse osmosis brine， specifically through bipolar membrane electrodialysis （BMED）， with the aim of recovering valuable acids and bases while improving the overall treatment efficiency. This research systematically examined the impact of various operational parameters on the performance of the BMED， including membrane stack voltage， membrane surface flow rate， product-to-raw material volume ratio， and current efficiency. The results revealed that the optimal performance was achieved when the membrane stack voltage was set at 24 V， the membrane surface flow rate was 3.540 cm/s， and the product-to-raw material volume ratio was maintained at 5：5. These findings provide valuable insights into the treatment of coal chemical wastewater， specifically in terms of enhancing the recovery of valuable chemicals from reverse osmosis brine. This research could serve as a sound reference for improving the efficiency of coal chemical wastewater treatment processes and mitigating the challenges associated with brine disposal.
The treatment of coal chemical wastewater has become a significant challenge， restricting the development of coal chemical enterprises. While the dual-membrane process of "nanofiltration + reverse osmosis" is commonly employed for separating organic substances and inorganic salts in wastewater， it's insufficient in treating the reverse osmosis brine generated during this process. This study focused on exploring a more efficient method to treat the reverse osmosis brine， specifically through bipolar membrane electrodialysis （BMED）， with the aim of recovering valuable acids and bases while improving the overall treatment efficiency. This research systematically examined the impact of various operational parameters on the performance of the BMED， including membrane stack voltage， membrane surface flow rate， product-to-raw material volume ratio， and current efficiency. The results revealed that the optimal performance was achieved when the membrane stack voltage was set at 24 V， the membrane surface flow rate was 3.540 cm/s， and the product-to-raw material volume ratio was maintained at 5：5. These findings provide valuable insights into the treatment of coal chemical wastewater， specifically in terms of enhancing the recovery of valuable chemicals from reverse osmosis brine. This research could serve as a sound reference for improving the efficiency of coal chemical wastewater treatment processes and mitigating the challenges associated with brine disposal.
2025,
43(4):
182-193.
doi: 10.13205/j.hjgc.202504018
Abstract:
To improve the stability of metal-organic framework （MOF） materials for the adsorption of organic compounds and heavy metal mixed pollutants， this study applied the ice-templating and freeze-drying method to prepare a hybrid foam of ZIF-8/CMC， with carboxymethyl cellulose （CMC） acting as the crosslinking agent. The structural and functional properties of ZIF-8/CMC were thoroughly characterized using advanced analytical techniques， including scanning electron microscopy （SEM）， X-ray diffraction （XRD）， nitrogen adsorption-desorption tests， and thermogravimetric analysis （TG）. Additionally， the adsorption performance， adsorption mechanisms， and recycling stability of ZIF-8/CMC for tetracycline （TC） and Cu2+ were systematically investigated. The results demonstrated that CMC was successfully loaded into the channels of ZIF-8， forming an irregular three-dimensional structure that significantly enhanced the material's adsorption capacity for pollutants and the recycling performance. The optimal adsorption conditions for ZIF-8/CMC were determined as follows： an adsorption time of 16 hours， an initial pollutant concentration of 30 mg/L，a pH value of 6， and an adsorbent dosage of 50 mg. Under these conditions， the adsorption capacities of ZIF-8/CMC for TC and Cu2+ reached remarkable levels of 78.75 mg/g and 79.71 mg/g， respectively. The adsorption isotherm process of ZIF-8/CMC was found to comply well with the Langmuir model， indicating monolayer adsorption on a homogeneous surface. Furthermore， thermodynamic studies revealed that the adsorption of TC and Cu2+ by ZIF-8/CMC was a spontaneous， exothermic， and orderly process， suggesting a thermodynamically favorable and orderly adsorption mechanism.In terms of reusability， ZIF-8/CMC exhibited excellent cyclic stability， maintaining a removal rate of 75% above for both TC and Cu2+ even after five consecutive adsorption cycles. This highlights the material's robust performance and potential for repeated use in practical applications. The ZIF-8/CMC adsorbent demonstrated high selective adsorption performance for TC and Cu2+， along with exceptional reusability in cyclic adsorption processes. These properties make it a highly promising candidate for the treatment of pharmaceutical wastewater， where the removal of organic pollutants and heavy metals is critical. Moreover， the unique structural and functional characteristics of ZIF-8/CMC suggest its potential for broader environmental applications， such as the remediation of industrial effluents containing complex pollutants mixtures. Future research sould focus on scaling up the synthesis of ZIF-8/CMC， optimizing its performance in real-world conditions， and exploring its effectiveness in removing other emerging contaminants. The findings of this study not only advance the understanding of MOF-based hybrid materials but also pave the way for their practical implementation in sustainable water treatment technologies.
To improve the stability of metal-organic framework （MOF） materials for the adsorption of organic compounds and heavy metal mixed pollutants， this study applied the ice-templating and freeze-drying method to prepare a hybrid foam of ZIF-8/CMC， with carboxymethyl cellulose （CMC） acting as the crosslinking agent. The structural and functional properties of ZIF-8/CMC were thoroughly characterized using advanced analytical techniques， including scanning electron microscopy （SEM）， X-ray diffraction （XRD）， nitrogen adsorption-desorption tests， and thermogravimetric analysis （TG）. Additionally， the adsorption performance， adsorption mechanisms， and recycling stability of ZIF-8/CMC for tetracycline （TC） and Cu2+ were systematically investigated. The results demonstrated that CMC was successfully loaded into the channels of ZIF-8， forming an irregular three-dimensional structure that significantly enhanced the material's adsorption capacity for pollutants and the recycling performance. The optimal adsorption conditions for ZIF-8/CMC were determined as follows： an adsorption time of 16 hours， an initial pollutant concentration of 30 mg/L，a pH value of 6， and an adsorbent dosage of 50 mg. Under these conditions， the adsorption capacities of ZIF-8/CMC for TC and Cu2+ reached remarkable levels of 78.75 mg/g and 79.71 mg/g， respectively. The adsorption isotherm process of ZIF-8/CMC was found to comply well with the Langmuir model， indicating monolayer adsorption on a homogeneous surface. Furthermore， thermodynamic studies revealed that the adsorption of TC and Cu2+ by ZIF-8/CMC was a spontaneous， exothermic， and orderly process， suggesting a thermodynamically favorable and orderly adsorption mechanism.In terms of reusability， ZIF-8/CMC exhibited excellent cyclic stability， maintaining a removal rate of 75% above for both TC and Cu2+ even after five consecutive adsorption cycles. This highlights the material's robust performance and potential for repeated use in practical applications. The ZIF-8/CMC adsorbent demonstrated high selective adsorption performance for TC and Cu2+， along with exceptional reusability in cyclic adsorption processes. These properties make it a highly promising candidate for the treatment of pharmaceutical wastewater， where the removal of organic pollutants and heavy metals is critical. Moreover， the unique structural and functional characteristics of ZIF-8/CMC suggest its potential for broader environmental applications， such as the remediation of industrial effluents containing complex pollutants mixtures. Future research sould focus on scaling up the synthesis of ZIF-8/CMC， optimizing its performance in real-world conditions， and exploring its effectiveness in removing other emerging contaminants. The findings of this study not only advance the understanding of MOF-based hybrid materials but also pave the way for their practical implementation in sustainable water treatment technologies.
2025,
43(4):
194-203.
doi: 10.13205/j.hjgc.202504019
Abstract:
The impacts and mechanisms of methane （CH4） production and emission in municipal gravity-flow sewer system under dual-carbon constraints is a critical topic in wastewater management， serving as the foundation for advancing pollution reduction and carbon mitigation technologies. This study examined the hydrodynamic characteristics of municipal gravity-flow sewers， focusing on spatial and temporal scales. The results demonstrated that the water flow exhibited unsteady， turbulent， and secondary-flow characteristics. The study then explored how hydrodynamic factors such as pipeline flow rate， hydraulic residence time（HRT）， and shear stress influenced methane production and emission. The findings indicated that methane production exhibited an increasing trend and subsequently decreased with an increase in flow rate and shear force within a specified range， demonstrating proportionality with the HRT. It also analyzed the effects of hydrodynamics on methane production and emission， considering pipeline sediment settlement， erosion， nutrient， and dissolved oxygen transfer. It revealeds that flow velocity and shear stress affected sediment formation and subsequent suspension， and hydrodynamic force introduced air into water， altering dissolved oxygen levels and impacting the anaerobic environment for methanogenic archaea. These actions also influenced nutrient mass transfer and microbial growth. The mechanisms by which hydrodynamics influence methane production and emission in wastewater pipelines were elucidated from three perspectives： gas migration， microbial community distribution， and metabolic pathways. Future research directions include investigating the unsteady and turbulent characteristics within wastewater pipelines， erosion of microbial-attached sediment surfaces， and the mechanisms by which hydrodynamics affect methane production and emission. These findings contribute to understanding methane production and emission processes in gravity-flow sewers and support the development， construction， operation， and maintenance of low-carbon sewage networks.
The impacts and mechanisms of methane （CH4） production and emission in municipal gravity-flow sewer system under dual-carbon constraints is a critical topic in wastewater management， serving as the foundation for advancing pollution reduction and carbon mitigation technologies. This study examined the hydrodynamic characteristics of municipal gravity-flow sewers， focusing on spatial and temporal scales. The results demonstrated that the water flow exhibited unsteady， turbulent， and secondary-flow characteristics. The study then explored how hydrodynamic factors such as pipeline flow rate， hydraulic residence time（HRT）， and shear stress influenced methane production and emission. The findings indicated that methane production exhibited an increasing trend and subsequently decreased with an increase in flow rate and shear force within a specified range， demonstrating proportionality with the HRT. It also analyzed the effects of hydrodynamics on methane production and emission， considering pipeline sediment settlement， erosion， nutrient， and dissolved oxygen transfer. It revealeds that flow velocity and shear stress affected sediment formation and subsequent suspension， and hydrodynamic force introduced air into water， altering dissolved oxygen levels and impacting the anaerobic environment for methanogenic archaea. These actions also influenced nutrient mass transfer and microbial growth. The mechanisms by which hydrodynamics influence methane production and emission in wastewater pipelines were elucidated from three perspectives： gas migration， microbial community distribution， and metabolic pathways. Future research directions include investigating the unsteady and turbulent characteristics within wastewater pipelines， erosion of microbial-attached sediment surfaces， and the mechanisms by which hydrodynamics affect methane production and emission. These findings contribute to understanding methane production and emission processes in gravity-flow sewers and support the development， construction， operation， and maintenance of low-carbon sewage networks.
2025,
43(4):
204-212.
doi: 10.13205/j.hjgc.202504020
Abstract:
A carbon emission inventory with high-spatial-resolution covering various fields is an important foundation for cities to implement precise carbon reduction policies. A method for city-level high-resolution CO2 emission inventories was proposed. Based on the provincial energy balance table， the economic and industrial indexes， population， roads， land use， and points of interest （POI） data were applied to establish a 1 km × 1 km CO2 emission inventory for Yingkou in northestern China. The results showed that the total CO2 emissions in Yingkou in 2020 reached 17.9327 million tons， consisting of 18.0084 million tons from energy consumption and -0.0722 million tons from land use. In terms of energy consumption emissions， the main sources were concentrated in manufacturing and coal consumption. CO2 emissions from manufacturing accounted for 69.94%， and CO2 emissions from coal consumption accounted for 53.56%. The spatial agglomeration effect of CO2 emissions was obvious. Among the 5550 grids， CO2 emissions from 18 extremely high-emission grids （≥ 50000 tons） accounted for 68% of the total city， while CO2 emissions from 37 high-emission grids （≥ 20000 tons） accounted for 90%. As for the space distribution of key industries， the emissions from the steel making and power thermal industries were relatively concentrated， whereas the grid emissions from the non-ferrous and non-metallic mineral product industries showed gradient differences.High-emission grids from other industries were mainly distributed in areas with rapid economic development and dense population. It is necessary to conduct localized research on emission factors， improve the basic data statistical system and CO2 accounting for key enterprises to enhance the accuracy of city-level CO2 emission inventories.
A carbon emission inventory with high-spatial-resolution covering various fields is an important foundation for cities to implement precise carbon reduction policies. A method for city-level high-resolution CO2 emission inventories was proposed. Based on the provincial energy balance table， the economic and industrial indexes， population， roads， land use， and points of interest （POI） data were applied to establish a 1 km × 1 km CO2 emission inventory for Yingkou in northestern China. The results showed that the total CO2 emissions in Yingkou in 2020 reached 17.9327 million tons， consisting of 18.0084 million tons from energy consumption and -0.0722 million tons from land use. In terms of energy consumption emissions， the main sources were concentrated in manufacturing and coal consumption. CO2 emissions from manufacturing accounted for 69.94%， and CO2 emissions from coal consumption accounted for 53.56%. The spatial agglomeration effect of CO2 emissions was obvious. Among the 5550 grids， CO2 emissions from 18 extremely high-emission grids （≥ 50000 tons） accounted for 68% of the total city， while CO2 emissions from 37 high-emission grids （≥ 20000 tons） accounted for 90%. As for the space distribution of key industries， the emissions from the steel making and power thermal industries were relatively concentrated， whereas the grid emissions from the non-ferrous and non-metallic mineral product industries showed gradient differences.High-emission grids from other industries were mainly distributed in areas with rapid economic development and dense population. It is necessary to conduct localized research on emission factors， improve the basic data statistical system and CO2 accounting for key enterprises to enhance the accuracy of city-level CO2 emission inventories.
2025,
43(4):
213-221.
doi: 10.13205/j.hjgc.202504021
Abstract:
The problem of unstable particulate emission concentration in the porous electrode electrostatic precipitator （ESP） used for flue gas purification in the header of the sintering machine， is usually caused by the effect of secondary dust lifting. the optimization of the structure and parameters of the vibration device can improve the performance of the dust removal and ensure the high efficiency and stable operation of the porous electrode ESP. Through physical experiments and numerical analysis， the effects of vibration hammer， vibration anvil mass， hammer arm length and suspension method on the tangential vibration acceleration on the plate surface were investigated. The results showed that： with the increase of the vibrating hammer mass， the average tangential acceleration of the plate surface increased by 50%； the tangential acceleration of the plate surface decreased with the increase of the vibrating anvil mass； with an increase of the vibrating anvil mass from 4.4 kg to 5.4 kg， the average tangential acceleration of the plate surface reduced by 10%， and when the vibrating anvil mass exceeded 5.4 kg， the decrease extent reduced significantly； the average tangential acceleration of the plate surface increased with the length of the vibrating hammer arm. The average tangential vibration acceleration of the plate surface increased by 18.2% with the increase of the arm length of the hammer； the tangential vibration acceleration of the plate surface with single-point eccentric suspension was larger than the minimum design value of vibration acceleration of 180 g， and the relative root-mean-square was less than 0.4. In order to meet the requirements of the flue gas de-dusting of the header of the industrial sintering machine， it was optimal to adopt the vibration device with single-point eccentric suspension， a vibration hammer with the mass of 14.83 kg， the length of the arm of 335 mm， in combination with a vibration anvil with a mass of 4.4 kg. This study can provide a reference basis for the design and application of porous electrode ESPs in the ultra-low emission design of sinter headers.
The problem of unstable particulate emission concentration in the porous electrode electrostatic precipitator （ESP） used for flue gas purification in the header of the sintering machine， is usually caused by the effect of secondary dust lifting. the optimization of the structure and parameters of the vibration device can improve the performance of the dust removal and ensure the high efficiency and stable operation of the porous electrode ESP. Through physical experiments and numerical analysis， the effects of vibration hammer， vibration anvil mass， hammer arm length and suspension method on the tangential vibration acceleration on the plate surface were investigated. The results showed that： with the increase of the vibrating hammer mass， the average tangential acceleration of the plate surface increased by 50%； the tangential acceleration of the plate surface decreased with the increase of the vibrating anvil mass； with an increase of the vibrating anvil mass from 4.4 kg to 5.4 kg， the average tangential acceleration of the plate surface reduced by 10%， and when the vibrating anvil mass exceeded 5.4 kg， the decrease extent reduced significantly； the average tangential acceleration of the plate surface increased with the length of the vibrating hammer arm. The average tangential vibration acceleration of the plate surface increased by 18.2% with the increase of the arm length of the hammer； the tangential vibration acceleration of the plate surface with single-point eccentric suspension was larger than the minimum design value of vibration acceleration of 180 g， and the relative root-mean-square was less than 0.4. In order to meet the requirements of the flue gas de-dusting of the header of the industrial sintering machine， it was optimal to adopt the vibration device with single-point eccentric suspension， a vibration hammer with the mass of 14.83 kg， the length of the arm of 335 mm， in combination with a vibration anvil with a mass of 4.4 kg. This study can provide a reference basis for the design and application of porous electrode ESPs in the ultra-low emission design of sinter headers.
2025,
43(4):
222-231.
doi: 10.13205/j.hjgc.202504022
Abstract:
In the era of concurrent demands for both economic development and ecology preservation， emissions of civil aviation are gradually receiving more and more attention. Among all the monitoring methods for civil aviation emissions， the emission inventory method is undoubtedly an important part. In this study， global bibliometric and visualized analysis were applied to quantitatively analyze 1704 publications related to emission inventory of civil aviation in the Web of Science Core Collection database from 2003 to 2022. The results showed that： 1） over the past two decades， the annual number of publications has increased steadily， indicating an increasing attention on aviation exhaust emission inventories； 2） the United States has dominated in both publication quantity and impact； 3） whether from the distribution of institutions or journals， the United States and Britain possess the highest authority； 4） the mainstream research directions were air quality models， different gaseous pollutants from civil aviation exhaust and their impacts to environment and climate； in the first decade， the research focus was relatively narrow and fragmented， while in the last decade， a large number of interdisciplinary research such as the whole life cycle and biofuels has emerged； 5） with the field’s development， the research hotspots in the field of aviation emission inventories in the coming years are likely to focus on green and sustainable directions， such as carbon emission reduction and sustainable aviation fuels. The research focus in this field is expected to evolve from monitoring individual exhaust pollutants to improving air quality models， and eventually advancing green sustainable aviation. The cooperation among countries， institutions， and research fields will become closer. Compared with some developed countries， the citation rate of China's research results is low currently， which is mainly due to the late start of China's civil aviation exhaust inventory research， less mature technologies， and insufficient research depth. Therefore， China still needs to strengthen the win-win situation and cooperation with foreign institutions.
In the era of concurrent demands for both economic development and ecology preservation， emissions of civil aviation are gradually receiving more and more attention. Among all the monitoring methods for civil aviation emissions， the emission inventory method is undoubtedly an important part. In this study， global bibliometric and visualized analysis were applied to quantitatively analyze 1704 publications related to emission inventory of civil aviation in the Web of Science Core Collection database from 2003 to 2022. The results showed that： 1） over the past two decades， the annual number of publications has increased steadily， indicating an increasing attention on aviation exhaust emission inventories； 2） the United States has dominated in both publication quantity and impact； 3） whether from the distribution of institutions or journals， the United States and Britain possess the highest authority； 4） the mainstream research directions were air quality models， different gaseous pollutants from civil aviation exhaust and their impacts to environment and climate； in the first decade， the research focus was relatively narrow and fragmented， while in the last decade， a large number of interdisciplinary research such as the whole life cycle and biofuels has emerged； 5） with the field’s development， the research hotspots in the field of aviation emission inventories in the coming years are likely to focus on green and sustainable directions， such as carbon emission reduction and sustainable aviation fuels. The research focus in this field is expected to evolve from monitoring individual exhaust pollutants to improving air quality models， and eventually advancing green sustainable aviation. The cooperation among countries， institutions， and research fields will become closer. Compared with some developed countries， the citation rate of China's research results is low currently， which is mainly due to the late start of China's civil aviation exhaust inventory research， less mature technologies， and insufficient research depth. Therefore， China still needs to strengthen the win-win situation and cooperation with foreign institutions.
2025,
43(4):
232-241.
doi: 10.13205/j.hjgc.202504023
Abstract:
This research proposes a design strategy for an intelligent classification system for domestic garbage based on the improved YOLOv5 algorithm. The aim is to enhance both real-time and accuracy in garbage category identification during the domestic garbage sorting process， addressing the issues of difficult domestic garbage classification， low resource recycling rate， and serious environmental pollution. The algorithm uses YOLOv5 as the base network， replaces the C3 module in the original YOLOv5 architecture with the C3Ghost module of GhostNet， employs an updated loss function SIoU， and utilizes the cascade structure of the Ghost convolution combined with the CBAM attention mechanism to achieve the dual goals of lightweighting the backbone network and improving the model performance. The experimental results demonstrated that the algorithm reduced the weight of the network from 7.111 M to 4.039 M compared with the YOLOv5 base network. Additionally， it improved the frame rate from 73.8 FPS to 82.0 FPS， lightened the model by 50%， enhanced portability to mobile devices， and exhibitsed robust performance and detection capabilities. The system employs a single box with multiple points of interaction， integrating technologies such as infrared sensing， laser distance measurement， image recognition， motor drive and others to develop an intelligent classification dustbin featuring automated opening and closing， bag-breaking， sorting， separation of oil and water residue， and overflow feedback capabilities. The bin is capable of effectively handling a wide range of domestic garbage materials. It can effectively process all types of domestic garbage while preventing contact with potential pathogens and enabling intelligent garbage classification. This system not only reduces labor costs but also facilitates material recycling， thereby protecting the human ecological environment.
This research proposes a design strategy for an intelligent classification system for domestic garbage based on the improved YOLOv5 algorithm. The aim is to enhance both real-time and accuracy in garbage category identification during the domestic garbage sorting process， addressing the issues of difficult domestic garbage classification， low resource recycling rate， and serious environmental pollution. The algorithm uses YOLOv5 as the base network， replaces the C3 module in the original YOLOv5 architecture with the C3Ghost module of GhostNet， employs an updated loss function SIoU， and utilizes the cascade structure of the Ghost convolution combined with the CBAM attention mechanism to achieve the dual goals of lightweighting the backbone network and improving the model performance. The experimental results demonstrated that the algorithm reduced the weight of the network from 7.111 M to 4.039 M compared with the YOLOv5 base network. Additionally， it improved the frame rate from 73.8 FPS to 82.0 FPS， lightened the model by 50%， enhanced portability to mobile devices， and exhibitsed robust performance and detection capabilities. The system employs a single box with multiple points of interaction， integrating technologies such as infrared sensing， laser distance measurement， image recognition， motor drive and others to develop an intelligent classification dustbin featuring automated opening and closing， bag-breaking， sorting， separation of oil and water residue， and overflow feedback capabilities. The bin is capable of effectively handling a wide range of domestic garbage materials. It can effectively process all types of domestic garbage while preventing contact with potential pathogens and enabling intelligent garbage classification. This system not only reduces labor costs but also facilitates material recycling， thereby protecting the human ecological environment.
2025,
43(4):
242-250.
doi: 10.13205/j.hjgc.202504024
Abstract:
Fly ash， red mud， and sludge were utilized as raw materials to prepare the ceramsite by the sintering method in this research， to investigate the adsorption effect of ceramsite and mechanism for Pb2+ removal. The optimal conditions for preparing ceramsite of fly ash， red mud， and sludge were determined by using the response surface method on the basis of the insights gained from preliminary single factor experiments. In order to investigate the adsorption characteristics of the ceramsite on low concentrations of Pb2+ in water， characterization analysis was conducted on the ceramsite， combined with the adsorption kinetics and adsorption isotherm model fitting. The results showed that the response surface model had a good prediction performance， and the relative error between the measured values and the predicted values was 1.75%. The influence of each preparation factor on the adsorption effect of Pb2+ on ceramsite followed a descending order of sintering temperature， sintering time and preheating temperature. The ceramsite prepared in the conditions of raw material ratio （fly ash：red mud：sludge） of 75∶20∶5， preheating temperature of 359℃， sintering temperature of 1053℃， and sintering time of 9 min had the best adsorption performance， and the removal rate and adsorption capacity of Pb2+ at an initial concentration of 10 mg/L by the ceramsite were found to be 91.05% and 9.11 mg/g， respectively. The adsorption process of Pb2+ with an initial concentration of 5， 10mg/L by ceramsite， was in accordance with the quasi secondary kinetic model （with the R2 of 0.9992 and 0.9981， respectively） and Langmuir adsorption isothermal model （with the R2 of 0.9986 and 0.9967， respectively）. The functional groups such as O—H， C＝O， C＝C， Si—O and Al—O played an important role in the adsorption process of Pb2+ by ceramsite. The crystallinity of ceramsite mineral components decreased after the adsorption of Pb2+.
Fly ash， red mud， and sludge were utilized as raw materials to prepare the ceramsite by the sintering method in this research， to investigate the adsorption effect of ceramsite and mechanism for Pb2+ removal. The optimal conditions for preparing ceramsite of fly ash， red mud， and sludge were determined by using the response surface method on the basis of the insights gained from preliminary single factor experiments. In order to investigate the adsorption characteristics of the ceramsite on low concentrations of Pb2+ in water， characterization analysis was conducted on the ceramsite， combined with the adsorption kinetics and adsorption isotherm model fitting. The results showed that the response surface model had a good prediction performance， and the relative error between the measured values and the predicted values was 1.75%. The influence of each preparation factor on the adsorption effect of Pb2+ on ceramsite followed a descending order of sintering temperature， sintering time and preheating temperature. The ceramsite prepared in the conditions of raw material ratio （fly ash：red mud：sludge） of 75∶20∶5， preheating temperature of 359℃， sintering temperature of 1053℃， and sintering time of 9 min had the best adsorption performance， and the removal rate and adsorption capacity of Pb2+ at an initial concentration of 10 mg/L by the ceramsite were found to be 91.05% and 9.11 mg/g， respectively. The adsorption process of Pb2+ with an initial concentration of 5， 10mg/L by ceramsite， was in accordance with the quasi secondary kinetic model （with the R2 of 0.9992 and 0.9981， respectively） and Langmuir adsorption isothermal model （with the R2 of 0.9986 and 0.9967， respectively）. The functional groups such as O—H， C＝O， C＝C， Si—O and Al—O played an important role in the adsorption process of Pb2+ by ceramsite. The crystallinity of ceramsite mineral components decreased after the adsorption of Pb2+.
2025,
43(4):
251-257.
doi: 10.13205/j.hjgc.202504025
Abstract:
The composition and chemical properties of MSWI fly ash are influenced by the type of incinerator and flue gas treatment process. Changes in acid removal processes and agents can cause variations in fly ash composition， posing challenges for future fly ash resource utilization. As the national WtE flue gas pollutants emissions standards become increasingly stringent， the application of sodium bicarbonate dry flue gas treatment （FGT） technology for acid removal is gradually increasing. However， there is a lack of research on the characteristics of fly ash produced by sodium bicarbonate dry acid removal process， and the prospects for the potential of fly ash resource utilization require further exploration. We compared fly ash samples from the existing SDA semi-dry process flue gas treatment system and the pilot-scale flue gas treatment system using the sodium bicarbonate dry method at a WtE plant in Zhejiang. Additionally， we also conducted fly ash desalination tests by water washing， analyzed the components and mass differences of two types of fly ash before and after water washing， tested the components of the washed water， and analyzed the cost differences and prospects for resource utilization of salts removal by water washing for the two types of fly ash. The results showed that， compared with the calcium-based fly ash from the original flue gas treatment process， the fly ash from the sodium bicarbonate dry method flue gas treatment process was primarily sodium-based. It contained soluble chloride salts， such as NaCl and Na2SO4， with a significant reduction in the content of CaCl2 and CaClOH. After water washing， the fly ash produced by the sodium bicarbonate dry method flue gas treatment process can be reduced by more than 79%， making it more promising for resource recovery. This is conducive for achieving deep reduction and resource recovery for fly ash.
The composition and chemical properties of MSWI fly ash are influenced by the type of incinerator and flue gas treatment process. Changes in acid removal processes and agents can cause variations in fly ash composition， posing challenges for future fly ash resource utilization. As the national WtE flue gas pollutants emissions standards become increasingly stringent， the application of sodium bicarbonate dry flue gas treatment （FGT） technology for acid removal is gradually increasing. However， there is a lack of research on the characteristics of fly ash produced by sodium bicarbonate dry acid removal process， and the prospects for the potential of fly ash resource utilization require further exploration. We compared fly ash samples from the existing SDA semi-dry process flue gas treatment system and the pilot-scale flue gas treatment system using the sodium bicarbonate dry method at a WtE plant in Zhejiang. Additionally， we also conducted fly ash desalination tests by water washing， analyzed the components and mass differences of two types of fly ash before and after water washing， tested the components of the washed water， and analyzed the cost differences and prospects for resource utilization of salts removal by water washing for the two types of fly ash. The results showed that， compared with the calcium-based fly ash from the original flue gas treatment process， the fly ash from the sodium bicarbonate dry method flue gas treatment process was primarily sodium-based. It contained soluble chloride salts， such as NaCl and Na2SO4， with a significant reduction in the content of CaCl2 and CaClOH. After water washing， the fly ash produced by the sodium bicarbonate dry method flue gas treatment process can be reduced by more than 79%， making it more promising for resource recovery. This is conducive for achieving deep reduction and resource recovery for fly ash.
2025,
43(4):
258-266.
doi: 10.13205/j.hjgc.202504026
Abstract:
Anaerobic digestion is an important technology for the harmless treatment and resource recovery of food waste.This paper focused on food waste， and explored its anaerobic fermentation process under continuous flow reaction conditions at different stages of medium temperature （MT）， intermediate temperature（IT）， and high temperature（HT）. It analyzed the succession changes of fermentation parameters and microbial communities at different stages of domestication， load increase， and high-load stable operation. The results showed that under the stable operation stage of 8 kg COD/（m3·d）， the biogas production efficiency of the IT group was significantly higher than that of the MT group and HT group. The volume gas production rate increased by more than 11%， and the biogas production rate increased by 12.53% and 9.56%， respectively. By analyzing the material parameters of each fermentation group， the VFAs / alkalinity parameters of the fermentation system in the IT group showed good buffering and loading impact resistance in the continuous operation of the system. In exploring the succession of microbial communities， the richness and diversity of the IT group were higher than that of the MT group and HT group under high organic load. Moreover， the differences in the dominant microbial community were greatly affected by temperature and organic load， and would evolve from the initially dominant acetic acidotrophic Methanosarcina to the hydrogenotrophic Methanoculleus， thereby making methane production in the system more stable.This paper further elucidated the differences in characteristics and microbial populations between intermediate-temperature and traditional medium-temperature fermentation， as well as high-temperature fermentation. It provided an efficient and stable new path for the anaerobic resource utilization of food waste and helped promote the creation of "waste-free city" in various regions of China.
Anaerobic digestion is an important technology for the harmless treatment and resource recovery of food waste.This paper focused on food waste， and explored its anaerobic fermentation process under continuous flow reaction conditions at different stages of medium temperature （MT）， intermediate temperature（IT）， and high temperature（HT）. It analyzed the succession changes of fermentation parameters and microbial communities at different stages of domestication， load increase， and high-load stable operation. The results showed that under the stable operation stage of 8 kg COD/（m3·d）， the biogas production efficiency of the IT group was significantly higher than that of the MT group and HT group. The volume gas production rate increased by more than 11%， and the biogas production rate increased by 12.53% and 9.56%， respectively. By analyzing the material parameters of each fermentation group， the VFAs / alkalinity parameters of the fermentation system in the IT group showed good buffering and loading impact resistance in the continuous operation of the system. In exploring the succession of microbial communities， the richness and diversity of the IT group were higher than that of the MT group and HT group under high organic load. Moreover， the differences in the dominant microbial community were greatly affected by temperature and organic load， and would evolve from the initially dominant acetic acidotrophic Methanosarcina to the hydrogenotrophic Methanoculleus， thereby making methane production in the system more stable.This paper further elucidated the differences in characteristics and microbial populations between intermediate-temperature and traditional medium-temperature fermentation， as well as high-temperature fermentation. It provided an efficient and stable new path for the anaerobic resource utilization of food waste and helped promote the creation of "waste-free city" in various regions of China.
