环境科学最新文献

{"title":"[Analysis and Evaluation of Hydrochemical Characteristics and Its Driving Force in Miyun Reservoir Basin].","authors":"Ke-Xuan Liu, Zheng-Shi Hua, Hui-Bo Liu, Wan-Lai Xue, Wen-Zhong Li, Xiao-Dan Liu, Dong-Mei Wang","doi":"10.13227/j.hjkx.202405151","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405151","url":null,"abstract":"<p><p>Miyun Reservoir is the most important surface drinking water source of the capital city, which is of great importance to guarantee the safety of the water supply in the capital city. A total of 39 surface water samples and 16 groundwater samples were collected in the field during the flood season in the Miyun Reservoir Basin to analyze the hydrochemical characteristics and to carry out water quality analysis and evaluation using the single-factor evaluation method and the Nemero Pollution Index and applying geo-detectors to analyze the spatial variability of the water quality indicators and spatial data, such as land use, meteorology, and socio-economics. The results showed that： ① Surface water and groundwater had similar hydrochemical characteristics, including close hydraulic connection, homology of component sources, and uneven spatial distribution of ion concentration, and three principal component factors could be extracted from surface water and groundwater hydrochemical ions, respectively, which were mainly affected by the combination of human activities and rock weathering. ② Water quality evaluation results showed that there were 15 water quality sampling points to meet the surface water class II standards and 14 water quality sampling points to meet the surface water class III standards, and there were seven water quality sampling points for the surface water class Ⅳ standards, two for class V, and one for inferior class V. According to the Nemero integrated pollution index classification of water quality categories, class I had 23, class II had 14, and class III and class V each had 1. The permanganate index was the main exceeding index, revealing that the surface water faced the risk of organic pollution. ③ Human activities were the main factors affecting the river water quality differentiation, with the explanatory power factor <i>q</i>-value of 0.204 9, and rainfall and elevation also had a significant impact on the water quality changes. Two-factor interaction significantly increased the explanatory power of the spatial differentiation of water quality in the river, which manifested itself as an enhancement or nonlinear enhancement, reflecting that the water quality of the basin level was affected by several factors together.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2732-2744"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102777","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":"[Effects of Mine Remediation Mode on Microbial Community Structure and Function in the Rhizosphere Soil of <i>Robinia pseudoacacia</i>].","authors":"Feng Yan, Xin Zhao, Yu-Pu Zhao, Xing-Yu Wang, Yu-Tong Zhang, Yue-Bing Liang, Ya-Xuan Wen, Ya-Heng Chen","doi":"10.13227/j.hjkx.202405188","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405188","url":null,"abstract":"<p><p>To explore the effects of soil substrates （guest soil, iron tailings sand, and dump mixed soil） on plant rhizosphere microbial diversity and function of three mine restoration modes in the northern Hebei mining area of China, the soil environment, microbial community structure, and coupling mechanism of rhizosphere soil were explored by soil nutrient determination technology and high-throughput sequencing technology. The results showed that： ① The species and distribution of bacteria and fungi in the three soil substrates were similar. There were 2 402 species of bacteria, and the dominant bacterial community was Proteobacteria. There were 1 059 species of fungi, and the dominant fungal community was Ascomycota. Among them, the similarity between the guest soil and the dump soil was the highest, with a total of 102 species of bacteria and 180 species of fungi. ② Soil substrate environmental factors in different remediation modes had significant effects. The contents of pH, SOM, and TN in guest soil were significantly higher than those of other types of soil. The combined effect of soil enzyme activity and soil nutrients had the greatest impact on the bacterial community, while the single effect of soil nutrients had the greatest impact on the fungal community. ③ There were significant differences in the structure and function of bacterial and fungal communities in different substrates. At the genus level, the bacterial and fungal community structure was significantly correlated with pH and TK （<i>P</i>&lt;0.01）, and the fungal structure was significantly correlated with SUC （<i>P</i>&lt;0.05）. The bacterial function of tailings sand was similar to that of the dump, which was mainly chemoheterotrophic and oxidative heterotrophic. The functions of fungi in the three groups were similar, and the main functions of different trophic types differed. The conclusions of this study have theoretical importance for the effective and sustainable development of the subsequent ecological restoration work in the mining area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"3272-3286"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102624","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-temporal Variation and Driving Forces of Carbon Storage in Suzhou City Based on the PLUS-InVEST-Geodetector Model].","authors":"Zhou Zhang, Jing-Jing Liu, Quan Zhang, Chao Chen, Zhao-Hui Yang","doi":"10.13227/j.hjkx.202405077","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405077","url":null,"abstract":"<p><p>The changes in urban land use and land cover have profound impacts on carbon storage, directly affecting urban carbon balance and climate adaptation capacity. Taking Suzhou City as the study area, this study first conducts a transition matrix analysis of land use data from 2000 to 2020. Then, based on the modified carbon density coefficient coupled with the PLUS and InVEST models, predictions are made for the land use pattern of Suzhou City in 2030 under four scenarios （business-as-usual development, urban sprawl prevention, farmland protection, and ecological conservation）. The ecosystem carbon storage from 2000 to 2020 and in 2030 under the four scenarios in Suzhou City are accounted for and the impact of land cover changes on carbon storage is analyzed. Finally, the Geodetector model is used to analyze the spatial differentiation driving forces of carbon storage. This study explores the mechanisms of land use change on carbon storage in regions with high urbanization levels. The results are as follows： ① From 2000 to 2020, Suzhou City's land use pattern underwent significant changes, with a continuous reduction in farmland and woodland, and the conversion of farmland to construction land was especially prominent. ② From 2000 to 2020, Suzhou City lost 3 750 195.27 t of carbon storage. Farmland and water bodies were the main carbon sink areas in the study area, accounting for 39.93% and 33.65% of the total carbon storage, respectively. Additionally, Suzhou City's carbon storage exhibited a spatial distribution characteristic of \"gradual increase from north to south.\" ③ The impact of land use conversion on carbon storage in Suzhou City varied. From 2000 to 2020, farmland was converted out of 1 632.758 km², resulting in a cumulative loss of carbon storage of 3 916 241.609 t, accounting for 96.9% of the total loss. Conversions from water bodies, construction land, and unused land to other land types increased the total carbon storage by 131 184.929, 140 024.741, and 18 641.031 t, respectively. ④ From the perspective of carbon sequestration, the ecological conservation scenario was significantly advantageous compared to the other three scenarios, providing strong evidence and guidance for the formulation of Suzhou City's subsequent carbon reduction policies. ⑤ The spatial differentiation of carbon storage in Suzhou City was jointly influenced by various factors, with elevation, temperature, population density, and Normalized Difference Vegetation Index （NDVI） being the main influencing factors, among which NDVI had the strongest explanatory power, reaching 0.29.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2963-2975"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102799","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 of Polycyclic Aromatic Hydrocarbons in Fine Particulate Matter from Catering Sources and Differentiated Health Risks among Populations in Handan].","authors":"Xiao Xiao, Peng Qi, Hao-Yun Qi, Ke Gao","doi":"10.13227/j.hjkx.202404284","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404284","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The pollution of polycyclic aromatic hydrocarbons （PAHs） in fine particulate matter （PM&lt;sub&gt;2.5&lt;/sub&gt;） from catering sources is closely related to human health. However, existing studies have not yet clarified the actual pollution status of PAHs from catering sources and their differentiated health risks among populations in Handan. Focusing on 11 typical catering enterprises in Handan, this study analyzed the pollution characteristics of 16 types of PAHs under EPA optimal control from catering sources based on gas chromatography-triple quadrupole mass spectrometry. Dividing the population into 10 groups based on age and gender, we used Monte Carlo simulation to evaluate the differentiated health risks of PAHs from catering sources based on the daily intake assessment model and the incremental lifetime carcinogenic risk （ILCR） model. The results showed that the total mass concentration range of PAHs in PM&lt;sub&gt;2.5&lt;/sub&gt; from catering sources was 0.04-0.24 μg·m&lt;sup&gt;-3&lt;/sup&gt;. From a composition perspective, the average proportion of benzo[ghi]perylene （18.0%）, pyrene （17.8%）, and phenanthrene （12.8%） was relatively high. From a structural perspective, the proportion of 4-ring PAHs （29.2%-52.4%） was the highest. The mass concentration range of benzo[a]pyrene （BaP） in PM&lt;sub&gt;2.5&lt;/sub&gt; from catering sources was 0.001-0.011 μg·m&lt;sup&gt;-3&lt;/sup&gt;, which was higher than the World Health Organization limit and partially higher than China's annual average mass concentration limit for environmental BaP. The PAHs emitted from the western restaurant and barbecue restaurant were most likely to pose a carcinogenic risk to the population, with ILCR ranges of 0.67×10&lt;sup&gt;-6&lt;/sup&gt;-0.19×10&lt;sup&gt;-4&lt;/sup&gt; and 0.56×10&lt;sup&gt;-6&lt;/sup&gt;-0.16×10&lt;sup&gt;-4&lt;/sup&gt;, respectively. Exposed to the same PM&lt;sub&gt;2.5&lt;/sub&gt; from catering sources, the order of carcinogenic risk for different age groups was： children&gt;adolescents&gt;youth&gt;middle-aged&gt;elderly, and there was a weak gender difference in carcinogenic risk among the same age group. Approximately 30%-40% of children and 5%-15% of adolescents may face a higher carcinogenic risk than the entire adult population, and about 15%-25% of children may face a higher carcinogenic risk than that of other populations. Under lifelong exposure, the average value of life loss expectancy （LL） caused by PAHs in PM&lt;sub&gt;2.5&lt;/sub&gt; from catering sources to the population ranged from 1.2 to 24.1 minutes, with that of the western restaurant being 1.2 times that of the barbecue restaurant and 1.7 times that of northeastern cuisine. The research results have made up for the shortcomings of existing research regarding the vague understanding of the actual pollution status of catering sources in Handan and the relatively single population classifications for health risk assessment. It has also proposed prevention and control measures for key catering sources and populations and can provide comprehensive and detailed data support for the assessment o","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2673-2683"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102838","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 Apportionment of Heavy Metal Pollution in Farmland Soil of a Stone Coal Mining Area and Its Surrounding Area Based on APCS-MLR and PMF Models].","authors":"Tian-Hao Dong, Shu-Fang Pan, Ren-Jie Zhang, Li-Heng Jiang, Yan Guo, Xiong-Hui Ji, Yun-He Xie","doi":"10.13227/j.hjkx.202403278","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403278","url":null,"abstract":"<p><p>To investigate the potential risk of heavy metal contamination in agricultural soil resulting from stone coal mining activities, soil samples were collected and analyzed from a stone coal mining site and its surrounding areas. The findings revealed that： ① The risk of heavy metal pollution in the vicinity of the stone coal mining area was notably high, with 61.6% of sampling sites exhibiting moderate to severe pollution as determined by the Nemerow composite pollution index, including 21.9% showing significant levels of contamination. Soil contamination with Cd within the study area was particularly severe, with 71.5% and 18.5% of sampling sites exceeding risk screening and control values, respectively. Some soil samples also indicated potential risks associated with Cu and Zn, while individual samples showed excessive levels of As, Pb, Hg, and Ni. ② There were highly significant positive correlations observed between soil Cd, Zn, Ni, Cu, and As； As-Hg-Pb； as well as Cr-As-Ni-Hg pairs, respectively. No significant correlations were found between the Cd-Cr or Cd-Pb pairs； however, other combinations involving different heavy metals exhibited notable positive associations, suggesting similar sources for their pollution origins. ③ Three distinct sources contributing to soil heavy metal pollution within the study area were identified utilizing two receptor models-namely stone coal mining activities, atmospheric deposition events, and natural sources, such as weathering processes According to APCS-MLR model analysis results, soil concentrations of Cd-As-Cu-Zn-Ni were primarily affected by stone coal mining activities, while Pb-Hg were mainly influenced by atmospheric deposition events, and Cr was predominantly impacted by natural sources alone, with each source contributing approximately 43.2%, 31.5%, and 25.3%. PMF model outcomes generally aligned closely with these findings, suggesting soil Cd and Hg originated from combined effects related to both stone coal-mining activity and atmospheric depositions. Each source contributed rates around 45.0%, 34.5%, and 20.5%. This research underscores a substantial threat posed by heavy metal contamination in farmland soils adjacent to stone coal mines and highlights how employing multiple receptor models can provide more accurate determination regarding primary sources responsible for heavy metal pollutants present within each specific location.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"3209-3219"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102854","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 Characteristics of Nitrogen Non-point Source Pollution in Beijing-Tianjin-Hebei Region Based on PLUS and InVEST Models].","authors":"Shuai-Jun Yue, Guang-Xing Ji, Jun-Chang Huang, Ming-Yue Cheng, Jian-Xi Geng, Yu-Long Guo, Wei-Qiang Chen","doi":"10.13227/j.hjkx.202404297","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404297","url":null,"abstract":"<p><p>With the rapid development of the economy and rapid population growth, the destruction of the environment by humans is growing, and the discharge of various pollutants has seriously threatened the regional ecological security. First, based on the 2010 Beijing-Tianjin-Hebei land use data, the PLUS model was used to simulate the 2020 land use data and the accuracy of the simulation results was verified with the real data. The land use data of 2030-2050 under the sustainable development scenario （SSP119）, natural development scenario （SSP245）, and economic development scenario （SSP585） were simulated. Then, based on the InVEST model, the spatio-temporal changes of nitrogen pollution in the Beijing-Tianjin-Hebei Region from 2000 to 2020 and in the future under the SSP119, SSP245, and SSP585 scenarios from 2030 to 2050 were estimated. The results showed that the total nitrogen loads in the Beijing-Tianjin-Hebei Region were 41 300, 41 000, and 40 900 tons in 2000, 2010, and 2020, respectively and the total nitrogen load in the Beijing-Tianjin-Hebei Region showed a decreasing trend from 2000 to 2020. Compared with that in 2020, the total nitrogen load in the Beijing-Tianjin-Hebei Region in 2050 under the SSP119 scenario increased by 2 000 tons, the total nitrogen load in the Beijing-Tianjin-Hebei Region in 2050 under the SSP245 scenario increased by 3 200 tons, and the total nitrogen load in the Beijing-Tianjin-Hebei Region in 2050 under the SSP585 scenario decreased by 300 tons. Compared with the SSP245 and SSP119 scenarios, the development of SSP585 was more conducive to the reduction of nitrogen pollution in the Beijing-Tianjin-Hebei Region.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2767-2782"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102857","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":"[Basin Distribution and Ecological Risk of Microplastics in Surface Water Bodies in China].","authors":"Yu-Long Pan, Chong-Miao Zhang","doi":"10.13227/j.hjkx.202403221","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403221","url":null,"abstract":"<p><p>To gain a comprehensive understanding of the distribution of microplastics in surface waters in China and clarify the related ecological risks, data of surface water bodies, such as rivers, lakes, reservoirs, and estuaries in China from 2014 to 2023 were collected, and the potential ecological risk index method was used to comprehensively evaluate the ecological risk of microplastics in surface water bodies of ten major basins in China. The results showed that the rivers, lakes, reservoirs, and estuaries in different basins of China were all polluted by microplastics. The main materials were polypropylene and polyethylene, mainly colorless transparent fibers and fragments, and the size was mostly &lt;1 mm, but the abundance of microplastics was significantly different. The median abundance of microplastics in the surface water of rivers, lakes, and estuaries in each basin ranged from 628 to 35 804, 1 to 4 738, and 869 to 792 100 items·m^-3, respectively. The median abundance of microplastics in sediments ranged from 61 to 1 531, 19 to 1 236, and 120 to 1 228 items·kg^-1, respectively. From the total potential ecological risk index （PERI_tot） of microplastics in rivers, the Haihe River Basin was at high ecological risk （level Ⅳ）, while the Yellow River Basin and the Yangtze River Basin were at medium ecological risk （level Ⅲ）. The majority of PERI_tot in the rivers of the Haihe River Basin came from polyurethane, with a highest contribution rate of 99.88%, while the main contributors to the PERI_tot of rivers and lakes in the Yellow River and the Yangtze River Basin and the PERI_tot of the surface water in the Yellow River Estuary were polyvinyl chloride and polystyrene, respectively. Microplastic pollution on the surface water bodies of the southeast side of HU Huan-yong Line was crucial, whereas a few research reports were available on microplastics in the surface water bodies on the northwest side, and the pollution status remained unclear. The abundance of microplastics in surface water bodies in different regions was significantly positively correlated with the population density and local gross domestic product （<i>P</i>&lt;0.05）. The study shows the basin distribution characteristics and ecological risks of microplastics in surface water bodies in China, which can provide scientific basis for the prevention and control of microplastic pollution in surface water bodies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2694-2707"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102861","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":"[Method for Simultaneous Quantifying Five Types of Microplastics by Tubular Furnace Pyrolysis-thermal Desorption-gas Chromatography-mass Spectrometry].","authors":"Zhi-Xin Wu, Lin Liu, Ruo-Zhen Yu, Zi-Yi Deng, Gang Yang, Yu-Xuan Wu, Cheng-You Liu, Fan-Chen Liu, Bing Zhang, Ying Yang, Han-Yun Zheng, Zi-Ye Zhang, Jia-Nan Li, Lin-Yan Huang, Yu-Jue Yang, Ya-Xian Zhao, Gao-Feng Zhao, Li-Fei Zhang, Guo-Rui Liu, Ran Dai, Ya-Qing Liu, Shu-Wei Pei, Han-Yu Tang, Hong-Wei Wang, Jun-Min Gao, Abdul Qadeer, Li-Hui An, Xing-Ru Zhao","doi":"10.13227/j.hjkx.202401126","DOIUrl":"https://doi.org/10.13227/j.hjkx.202401126","url":null,"abstract":"<p><p>A susceptible method has been established to simultaneously quantify five types of microplastics greater than 0.22 μm across various environmental matrices, namely, polyethylene （PE）, polypropylene （PP）, polystyrene （PS）, polyvinyl chloride （PVC）, and polyethylene terephthalate （PET）. In detail, five types of microplastics were completely pyrolyzed within a tubular furnace. Pyrolyzates were captured using a Tenax TA absorbent. Subsequently, target compounds were rereleased in a thermal desorption instrument and transferred into gas chromatography/mass spectrometry （GC/MS）. The indicative compounds were filtered and selected to identify and quantify target microplastics. The instrument detection limits for the five types of microplastics ranged from 0.03 μg to 1.91 μg, whereas the method detection limits of target microplastics were 0.07-2.87 μg·L^-1 in water, 0.31-16.52 μg·g^-1 in soil/sediment, and 0.11-7.41 μg·g^-1 in the organism, respectively. The relative standard deviations of 3.31%-22.37%, recoveries of 74.21%-119.63%, and quantitative ranges of 3.7-75 μg for PS； 15-300 μg for PP, PVC, and PET； and 30-600 μg for PE were also implemented. Importantly, this method had simple requirements for sample pretreatment, avoided the interference of complex matrix, and improved the repeatability and reliability of results. Subsequently, the technique quantified target microplastics in water, soil, sediments, and biological tissue. The results showed that the total mass concentrations of five microplastics in water samples were 4.48-37.34 μg·L^-1 and 10.55-218.98 μg·g^-1 in soil and sediments, respectively, and 8.82-19.81 μg·g^-1 in biological samples. This present study provided a reliable technical guarantee for future investigation and monitoring of environmental microplastic pollution.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"3200-3208"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102828","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":"[Quantitative Analysis of the Spatial and Temporal Distribution Pattern of Net Primary Productivity of Vegetation in Hubei Province and Its Multiple Driving Forces].","authors":"Zhi-Gang Zhou, Ye Ding, Min-Li Wang, Hui-Cong Zhang, Shi-Yu Tian, Fan Huang, Feng Yan","doi":"10.13227/j.hjkx.202405158","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405158","url":null,"abstract":"<p><p>The health and stability of vegetation ecosystems in Hubei Province are important to the ecological security of the entire Yangtze River Basin. The spatio-temporal succession pattern of vegetation net primary productivity （NPP） in Hubei Province was analyzed by using the Sen trend+MK test, based on the Google earth engine （GEE） platform to obtain the MOD17A3HGF.061 vegetation NPP dataset from 2002 to 2020. The coefficient of variation and the Hurst index were then employed to elucidate the multiple driving mechanisms of NPP spatio-temporal differentiation. This was achieved through the use of optimal parameters-based geographical detectors （OPGD）, which were employed to clarify the multiple driving mechanisms of spatial and temporal variability of NPP. The results showed that： ① Between 2002 and 2020, vegetation NPP in Hubei Province showed an increasing trend with a rate of 2.423 8 g·m^-2·a^-1, spatially higher in the west and lower in the east； vegetation NPP was dominated by low volatility （about 79% of the total）, the trend of change was dominated by increase （about 89% of the total）, and future change was dominated by weak inverse persistence （about 62% of the total）. ② The main factors affecting the change in vegetation NPP in Hubei Province were secondary GDP, tertiary GDP, the number of high-tech enterprises, and elevation （all <i>q</i>-values were greater than 0.5）； the two-factor interactions all showed either a two-factor enhancement or a nonlinear enhancement, whereas evapotranspiration ∩ secondary GDP had the highest explanatory power of 0.74. ③ Except for the indicators of type variables, such as slope direction, vegetation type, rock type, soil type, and land use type, most of the other factors showed significant differences in their effects on vegetation NPP in Hubei Province. Economic factors, such as first production GDP in the interval of （1.03 million yuan, 1.16 million yuan）, the mean value of NPP in Hubei Province was the largest at 733.886 7 g·m^-2·a^-1, and the NPP showed a decreasing trend with the increase in these factors. For natural factors, such as elevation in the largest interval measured, in the interval of （1.16e+03 m, 2.71e+03 m）, the mean value of NPP in Hubei Province was the largest at 686.4599 g·m^-2·a^-1, and then the opposite trend was observed. ④ Most of the influencing factors were significantly different from each other on the NPP of vegetation in Hubei Province, indicating that these factors had different mechanisms of action in influencing the NPP of vegetation. The results of the study can provide a data basis for the formulation of policies on ecological protection and restoration of vegetation in Hubei Province and the Yangtze River Basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2997-3008"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102848","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 the Temporal and Spatial Evolution of Carbon Emissions in the Provincial Logistic Industry in China from the Perspective of Shared Responsibility].","authors":"Yi-Cheng Chen, Xiang-Long Li, Yuan-Yuan Zhang","doi":"10.13227/j.hjkx.202405050","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405050","url":null,"abstract":"<p><p>To reduce enterprise costs and achieve China's 2060 carbon neutrality goal at an early stage, this study analyzes in depth the spatial and temporal evolution of carbon emissions from the logistics industry in China's provinces and its influencing factors from the perspective of shared responsibility and on the basis of a multiregional input-output model. Using Moran's <i>I</i> index and local spatial autocorrelation model, we conducted a correlation analysis of logistics industry carbon emissions from 2012 to 2017. Additionally, based on the geographically weighted regression （GWR） model, we conducted an in-depth analysis of the spatiotemporal evolution and influencing factors of carbon emissions from the logistics industry across various provinces in China. The research results indicate that transportation carbon emissions exhibited significant spatial clustering characteristics. From 2012 to 2017, there were significant differences in the logistics industry carbon emissions among China's provinces, with a marked polarization. Provinces with higher economic levels had a lower proportion of carbon emissions associated with outbound trade and internal logistic industry demand. The <i>R</i>2 of the GWR model ranged from 0.625 715 to 0.765 095, whereas the <i>R</i>2 of the OLS model ranged from 0.476 970 to 0.716 380. Additionally, the AICc values of the GWR model were consistently lower than those of the OLS model, indicating that the GWR model provided a significantly better fit and could better explain the spatiotemporal heterogeneity between various influencing factors and logistics industry carbon emissions. The heterogeneity results of the influencing factors showed that logistic energy intensity, freight turnover, and logistic industry per capita GDP were significantly positively correlated with logistic industry carbon emissions. Therefore, the spatiotemporal heterogeneity of influencing factors on carbon emissions should be completely considered and differentiated emission reduction policies for different provinces should be formulated.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 5","pages":"2874-2885"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144102859","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}