环境科学最新文献

{"title":"[Pollution Evaluation and Risk Control Suggestion of Heavy Metals in Soil-crop System in Different Soil Parent Material Areas of Guangxi].","authors":"Zhao-Yi Li, Han Zhao, Wei Li, Zi-Ning Zhao","doi":"10.13227/j.hjkx.202308276","DOIUrl":"https://doi.org/10.13227/j.hjkx.202308276","url":null,"abstract":"<p><p>To study the heavy metal pollution and influencing factors of soils and crops in different parent material areas and provide the basis for the classification and control of cultivated land, a total of 1 326 soil surface samples and 46 crop seed-root soil samples were collected from Xingye County in the southeast of Guangxi. The enrichment characteristics of heavy metals in the soil-crop system of four soil-forming parent materials were compared and analyzed, and the influencing factors of Cd absorption by rice were studied. The comprehensive quality impact index method was used to evaluate the soil and crops in the study area, and the safe use of cultivated land was proposed according to the evaluation results. The results showed that in the four soil-forming parent material areas, only the carbonate rock parent material area showed obvious enrichment of heavy metals in the soil, especially Cd. According to the \"National Food Safety Standard for the Limit of Pollutants in Food\" （GB 2762-2022）, the excess rate of heavy metal Cd in rice seeds was 35.7%, and the other heavy metal rates were not exceeded. The bioconcentration coefficient of heavy metal Cd in rice from different parent material areas was as follows： quaternary sediment area &gt; carbonate parent material area &gt; clastic parent material area &gt; granite parent material area. The enrichment of Cd in rice was affected by soil pH and CaO. When the soil pH value was in the range of 5.5-6.5, the Cd content and exceeding rate of rice seed increased significantly. The evaluation results of soil-crop heavy metal pollution showed that the overall heavy metal risk in the study area was high, and the proportions of clean, mild, light, moderate, and heavy pollution were 23.91%, 10.87%, 17.39%, 17.39%, and 30.43%, respectively. Combined with the distribution of the comprehensive quality influence index and the pollution characteristics of different parent materials, the classification and control suggestions were put forward, which provided ideas for the safe utilization of polluted cultivated land.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5517-5525"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Effect of Meteorological Elements and Air Pollutants on Ozone in Yinchuan City Park].","authors":"Cong-Hui Wang, Guang-Yao Shi, Si-Qi Yang, Xi-Lu Ni, Li-Rong Yang, Li-Ping Ji","doi":"10.13227/j.hjkx.202309171","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309171","url":null,"abstract":"<p><p>To examine the underlying determinants of ozone （O₃） in Yinchuan's urban park during varying seasons and to ascertain the role played by meteorological events and air contaminants in influencing O₃ concentrations at high altitudes, data on O₃, meteorological factors, and air pollutants were collected through prolonged positional observations carried out at the Ningxia Yinchuan National Urban Ecosystem Research Station. Pearson correlation analysis and a structural equation model were utilized to investigate the spatio-temporal distribution patterns, trends, and the primary factors influencing O₃. The findings demonstrated a notable seasonal variability in O₃ levels in Yinchuan's urban park, displaying an \"unimodal type\" with the O₃ concentration peaking in summer （131.18 μg·m^-3） and bottoming out in winter （71.45 μg·m^-3）. Among the meteorological factors, the highest impact on O₃ was attributed to temperature and wind speed （temperature mainly through direct effects and wind speed mainly through indirect effects）. Conversely, air pollutants such as NO<i>_x</i> and SO₂ greatly affected O₃ primarily through direct effects. Wind speed was identified as the primary influencing factor on O₃ during spring and summer, potentially contributing 29% and 24.7%, respectively. Conversely, NO₂ was implicated as the primary factor during autumn and winter, with an estimated contribution of 26.6% and 29.7%, respectively. Thus, a structural equation model can efficiently reveal the primary determinants behind O₃ variations throughout various seasons, which could furnish a scientifically rigorous foundation and technical aid for mitigating and managing O₃ levels in high-altitude regions.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5149-5156"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Source and Influencing Factor Analysis of Soil Heavy Metals Based on PMF Model and GeoDetector].","authors":"Si-Jing Sun, Chun-Yu Dong, Hao Zhang, Hai-Chan Yang, Zu-Zhi Huang, Yu Han, Nai-Ming Zhang, Li Bao","doi":"10.13227/j.hjkx.202309255","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309255","url":null,"abstract":"<p><p>In Lijiang City, as a typical example, 93 soil samples were collected from the study area, and soil pH； organic matter； and heavy metals arsenic （As）, mercury （Hg）, copper （Cu）, zinc （Zn）, lead （Pb）, cadmium （Cd）, and chromium （Cr） were determined. We explored the sources of heavy metals in the study area by means of Positive Definite Matrix Factorization （PMF） modeling and analyzed the impact of influencing factors by combining seven heavy metals with 13 influencing factors in a GeoDetector. The results showed that the mean values of soil heavy metals <i>ω</i>（As）, <i>ω</i>（Hg）, <i>ω</i>（Cu）, <i>ω</i>（Zn）, <i>ω</i>（Pb）, <i>ω</i>（Cd）, and <i>ω</i>（Cr） in the study area were 17.55, 0.19, 86.75, 164.84, 28.95, 0.39, and 167.87 mg·kg^-1, respectively, which were greater than the background values of soils in Yunnan Province （except for As and Pb）. Regarding spatial distribution, the high values of Cu and Cr content were mainly concentrated in Yulong Naxi Autonomous County； the high value areas of As, Hg, Pb, and Cd were mainly concentrated in Ninglang Yi Autonomous County； and the high value of Zn content was mainly concentrated in Huaping County. Correlation analysis and PMF modeling revealed that the main sources of heavy metals As and Hg in the study area were industrial sources, Zn was from transportation pollution sources, Cr and Cu were from natural sources, and Cd and Pb were from agricultural sources. Further, the factor detector of the GeoDetector found that soil pH and organic matter （OC） had strong explanatory power for the content of seven heavy metals, and the interaction detector found that the results following the interaction of different influencing factors were nonlinear enhancement or two-factor enhancement, in which the interaction of OC and pH was the dominant factor for the spatial differentiation of heavy metals. This provides an important scientific basis for the protection of the soil environmental health and sustainable development in Lijiang City.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5474-5484"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Pollution Assessment and Source Apportionment of Heavy Metals in the Surrounding Soil of Typical Mining Areas in Tongling, Anhui Province].","authors":"Man-Li Lin, Zhen-Qi Hu, Wei-Hua Peng, Wen-Ling Ye, Chun-Lei Zhang, Xin-Rui Huang, Song Chen, He-Rong Gui","doi":"10.13227/j.hjkx.202307274","DOIUrl":"https://doi.org/10.13227/j.hjkx.202307274","url":null,"abstract":"<p><p>To study the level of heavy metal pollution and ecological risks in the soil around typical mining areas in Tongling, a total of 150 soil samples were collected from the study area. The content characteristics of 10 elements, namely, As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Fe, and Zn, in the soils were analyzed. Methods including enrichment factor, the geo-accumulation index, single-factor pollution index, Nemero comprehensive pollution index, and potential ecological risk index were used to evaluate the pollution status of heavy metals in the soil of the study area. The pollution sources of heavy metals in the soil were also analyzed using correlation analysis, cluster analysis, and principal component analysis. The results showed that except for Cr and Fe, the average contents of the other eight heavy metal elements were higher than the soil background values in the study area. Pb, Zn, As, Cu, and Cd had a high degree of variation and were significantly affected by external interference. The spatial distribution showed that both Cr and Ni showed a decreasing trend from the edge to the central region, whereas the other eight heavy metals showed a decreasing trend from the central region to the surrounding areas. The pollution level of Cd and Cu in the soil of the research area was relatively severe. The overall ecological risk was at a medium to low level. Cd and Hg were the main contributing factors. As, Cd, Cu, Fe, Mn, Pb, and Zn mainly came from agricultural, industrial, and transportation sources, whereas Cr and Ni were mainly from natural sources. However, the sources of Hg were relatively complex. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution in metal mining areas, as well as the remediation of mine pollution.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5494-5505"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Pollution Characteristics, Source Analysis, and Health Risk Assessment of Heavy Metals in Soil and Crops in a Typical molybdenum Mining Area of Qinling Mountains].","authors":"Chao Zhang, Feng He, Zi-Yu Wang, Meng-Yao Yuan, Pan-Min-Wang Lai, Jun-Kang Guo","doi":"10.13227/j.hjkx.202309090","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309090","url":null,"abstract":"<p><p>This study focused on a molybdenum mining area in the Qinling Mountains （Shaanxi segment）. Crop and corresponding soil samples were collected from the vicinity of the mining area, and the concentrations of six heavy metals （Cr, Cu, Zn, As, Cd, and Pb） were determined. Soil heavy metal pollution was assessed using single-factor, comprehensive pollution, and geo-accumulation index methods. The primary sources of soil heavy metals were analyzed using the PMF model. A health risk assessment for soil and crops was conducted using the USEPA model. The results revealed severe pollution of agricultural soils by Cr, Cu, Zn, Cd, and Pb. Among these, Cr may have been primarily sourced from chrombismite nearby mining activities, contributing to 85.1% of the pollution. Cu and As were mainly sourced from agriculture, contributing 50.3% and 70.6%, respectively. Zn and Cd were primarily sourced from natural sources such as metal slag dust and rainwash from the mining area, contributing 73.5% and 48.7%, respectively. Pb was primarily sourced from transportation sources, contributing to 54.7% of the pollution. Crop metal contamination was especially severe for Cr, followed by Pb, whereas As and Cd contamination was relatively lower. Crops were significantly impacted by heavy metal pollution in agricultural soils. The health risk assessment indicated non-carcinogenic and carcinogenic risks for children due to soil heavy metals, whereas adults faced acceptable levels of risk. Both adults and children were exposed to highly significant non-carcinogenic and carcinogenic risks from heavy metals in the crops. Moreover, it is essential to implement effective measures to control heavy metal pollution from tailings to safeguard nearby residents, especially children, from adverse health risks.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5526-5537"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Spatial and Temporal Evolution Characteristics of Carbon Emission from Land Use and Influencing Factors in Gansu Province].","authors":"Zi-He Li, Dong-Mei Zhou, Jing Jiang, Jing Ma, Xiao-Yan Zhu, Peng Shi, Jun Zhang, Qing-Han Dong","doi":"10.13227/j.hjkx.202309123","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309123","url":null,"abstract":"<p><p>Land ecosystems are the largest carbon sink in the world, and land use change is one of the main factors leading to regional carbon emissions. By studying the spatiotemporal evolution characteristics and influencing factors of land use carbon emissions in Gansu Province from 2000 to 2020, this research aimed to provide a scientific basis and reference for promoting low-carbon land use and low-carbon economic development in Gansu Province. Using land use data and the greenhouse gas emission coefficient method, the study analyzed the growth trend of land use carbon emissions at the city-regional scale in Gansu Province, and the spatiotemporal evolution characteristics at the provincial scale, and identified the controlling factors through principal component analysis. The results showed that： ① From 2000 to 2020, land use carbon emissions in Gansu Province showed an overall increasing trend, from 24.289 3 million tons to 57.739 6 million tons. The first stage from 2000 to 2014 was a significant increase period, whereas the second stage from 2014 to 2020 was a stable and slightly decreasing period. Construction land was the main carbon source, and the carbon intensity continued to increase. ② Spatially, there was an \"east high, west low\" pattern, with carbon emissions in the eastern part of the province significantly higher than those in the western part. ③ Based on emission characteristics, Gansu Province could be divided into five types of carbon emission zones： slow growth, relatively slow growth, moderate growth, relatively fast growth, and rapid growth. ④ The main reasons for the continuous increase in land use carbon emissions in Gansu Province were economic development level, degree of land use, and energy consumption.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5040-5048"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Nitrogen Flow Characteristics of Agricultural Production and Consumption System in the Yangtze River Delta Region and Its Driving Factors].","authors":"Ze-Qian Zhang, Li Dong, Peng Liu, Ting-Ting Zhou, Li-Hui Sun","doi":"10.13227/j.hjkx.202310180","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310180","url":null,"abstract":"<p><p>To assess the impact of human activities on regional nitrogen （N） flow, based on the statistical data of 27 cities in the Yangtze River Delta Region （YRD）, N flow characteristics of the agricultural production and consumption system （APC） in the YRD from 2011 to 2020 were analyzed using substance flow analysis, and driving factors for N flow were analyzed using scenario analysis. The results showed that from 2011 to 2020, the mean N input intensity of the APC in the YRD was 194.6 kg·（hm²·a）^-1, which was more than five times the national average value； thus, the YRD was a hotspot of N input intensity in China. Chemical N fertilizer was the largest component of N input, and the YRD changed from a net export area of grain and animal products to a net import area due to the rapid growth of food consumption demand. The N output of the system was mainly N loss to the environment, accounting for 53.2% on average. The N use efficiency （NUE） of cropland and the N recycling ratio of the APC ranged from 38.7-42.2% and 15.8-21.5%, respectively, which were both at a low level. In addition, the total amount of N input and output of the APC both showed a parabolic decline trend, decreasing by 11.3% and 10.0%, respectively. Spatially, the overall N input intensity showed a pattern of \"high in the north and low in the south,\" and the spatial heterogeneity of N input intensity among cities was significant. Cities with high input intensity were mainly located in the north and east of Jiangsu, Shanghai, and northeast of Zhejiang. A significant positive spatial autocorrelation of the distribution of mean N input intensity was observed. The uncertainty of N flows was estimated using the error propagation equation. The uncertainty interval of N input and output ranged from 4.5% to 34.6%, which was roughly equivalent to the results of related studies, indicating that the model results were reliable. Based on the scenario analysis method, the decrease of the livestock scale led to a decrease of -0.27%-7.53% in the N input, making it the main reason for the decrease of total N input in the APC. Improving the NUE of cropland and re-establishing the linkage between cropland and livestock will help reduce N loss to the environment.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5451-5463"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Analysis of Spatial Distribution of Ecosystem Services and Driving Factors in Northeast China].","authors":"Jia-Qi Wang, Yan-Qiu Xing, Xiao-Qing Chang, Hong Yang","doi":"10.13227/j.hjkx.202311022","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311022","url":null,"abstract":"<p><p>Northeast China is an important ecological barrier in China, and an in-depth understanding of the spatial distribution in ecosystem services （ESs）, and the driving factors is crucial for realizing the subsequent management and protection of ESs. In the study, we quantitatively assessed the characteristics of spatial distribution in ESs in Northeastern China using the InVEST, RWEQ, and RUSLE models and identified the driving factors of ESs spatial distribution in conjunction with the geodetector based on meteorological data, remote sensing data, and socio-economic data. The results showed that the spatial distribution of ESs in Northeast China had obvious spatial heterogeneity. The high values of habitat quality （HQ）, carbon sequestration （CS） services, and soil conservation （SC） services were mainly distributed in the northern part of the four eastern leagues of the Inner Mongolia Autonomous Region, the northern part of Heilongjiang Province, and the eastern part of Northeast China, which were high in fraction vegetation cover, and low values were mainly found in southwestern and eastern Heilongjiang Province, western Jilin Province, and western Liaoning Province. The high values of the water yield （WY） service and wind prevention and sand fixation （WPSF） service were distributed in the east of the Inner Mongolia Autonomous Region and the east of Liaoning Province. The high values of WY services and WPSF services were distributed in the eastern part of Northeast China and the four eastern provinces of the Inner Mongolia Autonomous Region. According to the geodetector results, slope had the strongest explanatory power for the spatial distribution of SC services with a <i>q</i>-value of 0.31, land use/cover change had the strongest explanatory power for the spatial distribution of HQ and CS services with <i>q</i>-values of 0.64 and 0.52, respectively, and fraction vegetation coverage and annual precipitation had the strongest explanatory power for the spatial distribution of WPSF and WY services with <i>q</i>-values of 0.24 and 0.64, respectively, and there were interactions among all the driving factors. The spatial distribution of ESs in Northeast China was mainly influenced by natural factors. The results will provide a scientific basis for subsequent management and enhancement of ESs in Northeast China.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5385-5394"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Characteristics of Vegetation and Soil of Degraded Grasslands and Their Relationships in Xizang].","authors":"Qing-Wan Li, Jin-Kai Gu, Qing-Lin Li, Wan-Chi Li, Sheng-Jian Xiang, Guo-Yong Tang","doi":"10.13227/j.hjkx.202309141","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309141","url":null,"abstract":"<p><p>To investigate the characteristics of grassland degradation on a regional scale in Xizang, data on grassland degradation from the second grassland survey of Xizang and 12 vegetation and soil indicators from the National Tibetan Plateau Data Center were collected. Using ArcMap, 10 000 random sample points were selected on raster data （excluding non-grassland, desertification, and salinization data, leaving 7 949 valid sample points）. The multi-value extraction to-point method was applied to extract degradation and indicator data for each sample point. The characteristics of degraded grassland vegetation and soil and their relationships were analyzed in Xizang. Moreover, random forest modeling was conducted to predict the trend of grassland ecosystem changes. The results indicated that： ① The grasslands in Xizang were primarily composed of alpine steppe and alpine meadow types, accounting for 45.83% and 41.15% of the valid sample points, respectively. ② With the intensification of grassland degradation, the number of steppe-type species among the 17 grassland types gradually decreased, and the proportion of steppe dominated by species such as <i>Stipa purpurea</i> and <i>Carex moorcroftii</i> decreased, whereas the proportion of miscellaneous grasses and <i>Dasiphora fruticosa</i> increased. ③ As the degree of degradation increased, vegetation indicators generally showed a declining trend, with soil total nitrogen, total phosphorus, total potassium, and organic carbon decreasing, whereas soil pH and bulk density increased, and soil moisture content was not significant. ④ A positive correlation exists between soil moisture content, total nitrogen, total phosphorus, total potassium, organic carbon, vegetation cover, net primary productivity of vegetation, normalized difference vegetation index, aboveground biomass, and habitat quality. However, there was a negative correlation between pH and soil bulk density, and the correlation coefficients among various indicators decreased with the intensification of degradation. ⑤ The random forest simulation results showed that during the degradation process, the contribution rates of soil bulk density and habitat quality both exceeded 12%, with the model prediction accuracy reaching 78%. The study revealed that grassland degradation in Xizang was closely related to soil bulk density and habitat quality, indicating that higher soil bulk density or lower habitat quality may correspond to more severe grassland degradation. This provides a scientific basis for future grassland conservation and management.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5341-5350"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Identification of Priority Areas for Carbon Compensation in Chongqing Based on the Difference in Land Use Carbon Budget].","authors":"Huai-Yu Huang, Zhi-Wen Gong, Xiao-Juan Chen, Ran Huo, Qian-Qian Wang","doi":"10.13227/j.hjkx.202309069","DOIUrl":"https://doi.org/10.13227/j.hjkx.202309069","url":null,"abstract":"<p><p>This study aimed to explore the spatiotemporal patterns and balance characteristics of land use carbon budget, measure the value of carbon compensation, and delineate carbon compensation type zoning to provide scientific reference for further strengthening the connection between the construction of an ecological compensation system and the \"dual carbon\" target task. Based on the land cover data of Chongqing from 2000 to 2020, this study analyzed the spatiotemporal dynamics and balance relationship characteristics of the land use carbon budget. By using the revised carbon compensation model to measure the horizontal compensation standards, the normalized revealed comparative advantage （NRCA） index and K-means clustering analysis method were used to divide the carbon compensation area. The research results demonstrated that： ① the total land use carbon sequestration in Chongqing grew slowly from 2000 to 2020, whereas carbon emissions continued to increase significantly, and the net carbon emissions showed a distribution pattern of \"high in the center and low in the two wings.\" ② The average coefficient of variation in Chongqing was 0.602, and the carbon emission economy contributive coefficient and carbon ecological support coefficient were concentrated between 0.64-1.14 and 0.00-32.86, respectively. The difference in the contribution of carbon emissions and economic benefits between districts and counties was relatively small, but there was a mismatch between carbon supply and demand. ③ A significant spatial difference existed in the value of carbon compensation, with a total of 1.098 billion yuan in carbon payment and 634 million yuan in carbon compensation, respectively. Moreover, it was ultimately determined that there were eight key payment areas, seven general payment areas, three key compensation areas, and 20 general compensation areas. In conclusion, the research results can provide a reference for implementing differentiated development strategies in different types of carbon compensation regions, improve the collaborative governance capacity of the regional ecological environment, and promote the achievement of carbon neutrality goals.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 9","pages":"5027-5039"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}