环境科学最新文献

{"title":"[Spatial Temporal Coupling and Influencing Factors of Land-use Carbon Emissions and Ecosystem Service Value in the Yellow River Basin].","authors":"Hui-Ling Chen, Li-Sha Jiang, Zhen-Bo Wang","doi":"10.13227/j.hjkx.202404136","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404136","url":null,"abstract":"<p><p>The dramatic changes in land use caused by human economic activities have a profound impact on carbon emissions and ecosystem service value （ESV）. In order to explore the evolution characteristics of carbon emissions and ESV on the spatial and temporal scales， based on the land use data of the Yellow River Basin from 2000 to 2020， this study used spatial autocorrelation and multivariate Logit regression models to study the spatial and temporal characteristics and spatial correlation of total carbon emissions and ESV in counties of the Yellow River Basin， then to explore the influencing factors of spatial correlation. The research findings were as follows： ① In the past 20 years， the total amount of land use carbon emissions in the basin has shown an overall growth trend， and the increasing counties were concentrated in energy-rich areas such as Inner Mongolia， Ningxia， and northern Shaanxi. The total amount of ESV increased first and then decreased， and the high value counties were mainly distributed on the edge of the Yellow River Basin， among which Qumalai County in Qinghai Province had the most ESV. The low value counties of ESV were mainly located in the economically active urban agglomerations such as the Shandong Peninsula Region， Central Plains Region， Guanzhong Plains Region， and cities along the yellow river in Ningxia. The lowest value of ESV has always been located in Xi'an. ② There was a spatial negative correlation between total carbon emissions and total ESV. The number of counties with high carbon emissions and high ESV has been increasing， mainly distributed in southern Inner Mongolia， eastern Ningxia， and northern Shaanxi， which was related to the location near the Yellow River and energy development. The double low type was mainly located in the gully area of the Loess Plateau， which is connected to the strip from the east and west. The low-high class was contiguously distributed in Qinghai， Sichuan， and western Gansu， and some were island-like distributed around the double-low class. The number of high-low classes was increasing year by year， mainly located in the core city area. ③ In low ESV counties， regions with better economic development and higher population were more likely to increase their carbon emissions. Taking the low carbon emissions from land use as a reference， the per capita GDP， energy use efficiency， and rainfall were significantly negatively correlated with the high-high and high-low categories. This indicates that most counties with high carbon emissions had relatively dense populations and less rainfall， resulting in higher energy dependence. Additionally， there was a positive correlation between low-high class areas and total population. When located in areas with low land use carbon emissions， areas with higher ESV values tended to have more a concentrated population distribution. The increase in land reclamation rate may encroach on forests and grasslands that can provide higher ecosystem services","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3536-3545"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530095","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":"[Spatio-temporal Correlation Between Green Space Landscape Pattern and Carbon Emission in Three Major Coastal Urban Agglomerations].","authors":"Shi-Chen Fan, Hong-Yong Liu, Xiao-Ping Wang, Wei-Tao He","doi":"10.13227/j.hjkx.202403256","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403256","url":null,"abstract":"<p><p>In order to study the influence of urban green space landscape pattern on urban carbon emissions， nighttime lighting data， socioeconomic development data， and land use remote sensing data from 2000 to 2020 are used as the basis of analysis， and the three major coastal economically developed regions in China-Bohai Rim， Yangtze River Delta （YRD）， and Pearl River Delta （PRD） （nearly 100 cities in total） are used as the study area to analyze the spatial and temporal evolution characteristics of urban carbon emissions， as well as the influence of urban green space landscape pattern and its spatial and temporal changes. We also explored the influence of 10 urban green space landscape pattern indices on urban carbon emissions by using the random forest model and the Lasso regression model and further analyzed the four factors （number of patches， density of patches， dispersion of patches， and complexity of the shape of patches） that had a greater influence by using the spatio-temporal geographically weighted regression model， to explore the results of the spatial and temporal evolution of the influence of the urban green space landscape pattern on carbon emissions. The main findings of this study are as follows： ① Carbon emissions in the three study areas showed a slow growth trend， with the Bohai Rim showing a relatively fast growth rate. Carbon emissions were spatially aggregated in the selected study areas， with the majority of cities in the \"high and high\" agglomeration and the \"low and low\" agglomeration regions. There was spatial aggregation of carbon emissions in the selected study areas， with the majority of cities in \"high and high\" agglomeration and \"low and low\" agglomeration. The land-averaged carbon emissions in the three study areas were dispersed in all directions， with the economically strong cities as the core， and the overall carbon emission level was dispersed from the center to the surroundings. Additionally， along the rivers and coastal areas， carbon emissions were higher due to the concentration of ports， industrial zones， and cities. ② Landscape occupied by patches， number of patches， and density of patches had a significant negative correlation with urban carbon emissions， which indicates that the higher the number， density， and proportion of the landscape occupied by urban green space patches， the more it could hinder the growth of carbon emissions. On the contrary， the shape index and patch fragmentation index had a positive correlation with urban carbon emissions， indicating that the higher the shape complexity of urban green space patches and the higher the fragmentation degree of patches， the more it promoted the growth of urban carbon emissions. In addition， the aggregation index also showed a significant negative correlation with urban carbon emissions， which indicates that the higher the degree of aggregation of patches， the more it could inhibit the growth of carbon emissions. ③ The correlation between the green spa","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3509-3523"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530098","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 on Spatial-temporal Evolution and Driving Forces of Ecological Environment Quality in Different Frozen Ground Regions of the Three-River Headwater Region].","authors":"Shu-Zhen Li, Shang-Min Zhao","doi":"10.13227/j.hjkx.202406184","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406184","url":null,"abstract":"<p><p>Under the trend of frozen ground degradation caused by global warming， the study on the spatial-temporal evolution and driving forces of ecological environment quality in different frozen ground regions of the Three-River Headwater Region （TRHR） can provide a theoretical basis for regional ecological environment governance and ecological civilization construction. The remote sensing ecological index （RSEI） was constructed based on the MODIS data set of google earth engine from 2000 to 2022， and the spatial-temporal evolution of ecological environment quality in different frozen ground regions of the TRHR was explored by coefficient of variation， migration of gravity center， Theil-Sen slope estimation， Mann-Kendall test， and Hurst index. The driving force of natural and human factors on the spatial differentiation of ecological environment quality was compared and analyzed by Geodetector. The results showed that： ① From 2000 to 2022， the ecological environment quality of the TRHR was at a medium level as a whole， showing a spatial distribution pattern of \"low in the northwest and high in the southeast\" . Among them， the mean value of RSEI in different frozen ground regions were： predominantly continuous and island permafrost region （0.630） &gt; mountain permafrost region （0.624） &gt; middle-thick seasonally frozen ground region （0.587） &gt; predominantly continuous permafrost region （0.429）. ② In the past 23 years， the ecological environment quality of the TRHR was improved as a whole， the rising rate of RSEI was 0.001 5 a^-1， and the center of gravity of each grade of RSEI migrated to the interior of predominantly continuous permafrost region. More than 57.00% of the frozen ground regions showed an improvement trend， and the middle-thick seasonally frozen ground region reached 73.37%. ③ In the future， the ecological environment quality of the TRHR will be generally anti-sustained in the future， and the frozen ground regions will be dominated by a slight deterioration trend. The area of deterioration trend in the middle-thick seasonally frozen ground region will be the largest （61.95%）. ④ Precipitation was the dominant factor for the spatial differentiation of ecological environment quality in different frozen ground regions of the TRHR， and the influence of human factors was not significant. The driving force of the multi-factor synergy was greater than that of the single factor， and the interaction between precipitation and temperature factors had the strongest explanatory power. The driving factors of each frozen ground region were different， so ecological management policies should be tailored to local conditions in different frozen ground regions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3747-3759"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144529979","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":"[Urumqi Metropolis Circle \"Production-Living-Ecological Spaces\" Carbon Metabolism Analysis Based on Ecological Network Utility].","authors":"Wen-Jing Zhao, Cheng-Wu Wang","doi":"10.13227/j.hjkx.202405272","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405272","url":null,"abstract":"<p><p>Greenhouse gas emissions resulting from land use changes have become a significant factor affecting carbon emissions. With the global ecological environment issues becoming more prominent， attention to territorial spatial changes and carbon has also increased. Studying the carbon metabolism of \"production-living-ecological spaces\" can provide a basis for optimizing regional territorial space and promoting low-carbon development. Taking the Urumqi metropolitan area as an example， a classification system for \"production-living-ecological spaces\" and a vertical carbon metabolism model were constructed. Using the ecological network analysis model， the characteristics of the carbon metabolism evolution of \"production-living-ecological spaces\" from 2000 to 2020 were studied， the ecological relationships of carbon metabolism were evaluated， and their temporal and spatial changes were analyzed. The results showed： ① From 2000 to 2020， all space types underwent varying degrees of transfer， with forestry， grassland ecological spaces， and urban production and living spaces being particularly prominent. ② The net carbon flux and net carbon flow of the metropolitan area were both less than zero， indicating that the changes in \"production-living-ecological spaces\" had a negative impact on carbon metabolism， affecting the regional carbon balance. ③ Competitive relationships dominated from 2000 to 2015， while restrictive and exploitative relationships were predominant from 2015 to 2020. Ecological relationships primarily occurred in the west， northwest， central， and eastern parts of the study area. ④ Agricultural and ecological spaces were dominated by restrictive and exploitative relationships， agricultural and urban spaces by competition， and the dominant relationship between ecological and urban spaces shifted from competition to mutualism after 2015； the overall ecological efficiency index was less than 1. The research findings can provide a theoretical basis for the optimization of metropolitan \"production-living-ecological spaces\" and the implementation of carbon emission reduction， promoting high-quality， low-carbon regional development.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3556-3568"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530047","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":"[Water Quality Evolution in the Guishui River Section of Guanting Reservoir in Recent Years].","authors":"Lin-Lin Li, Rui Xia, Yan Chen, Jun-Feng Dai, Bing-Fen Cheng, Jun-de Ming","doi":"10.13227/j.hjkx.202405038","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405038","url":null,"abstract":"<p><p>Based on manual and automatic monitoring data of water quality at the Gujiaying section of the upstream of Guanting Reservoir （Guishui River section） from 2017 to 2023， this study comprehensive applied the Mann Kendall statistical test， STL trend decomposition， grey prediction model， and emission inventory statistics and other analysis methods， combined with literature research， and systematically revealed the water quality evolutions， influencing factors， and the impact of epidemic control and rainstorm events on water quality in the upper reaches of Guanting Reservoir. The results showed that： ① The period from 2017 to 2023 was the best time for the upstream inflow water quality of Guanting Reservoir in 50 years. Since Guanting Reservoir was restored as a backup water source in 2007， the inflow water quality was significantly improved， rising from Class Ⅳ to Class Ⅲ， reaching Class II in some months. The most significant decrease in NH₄⁺-N concentration from 2017 to 2023 may be related to the strengthened control of agricultural non-point sources and domestic sources in Yanqing District in recent years. ② During the lockdown period from March to May 2022， the concentrations of the main water quality indicators at the inlet section of Guanting Reservoir were significantly decreased year-on-year and month-on-month， and the epidemic control measures significantly improved the water quality. During the \"23·7\" rainstorm， the water quality at the inlet section of Guanting Reservoir deteriorated sharply， and turbidity， TP， and permanganate index responded fastest to the sudden change of upstream flow. ③ Agricultural non-point sources and domestic sewage were the main emissions affecting the water quality of the upstream section of Guanting Reservoir， with both contributing over 99%. The predicted water quality by the end of the 14th Five Year Plan period would be generally stable in Class Ⅱ-Ⅲ. The next step should focus on the variations in water quality at the inlet section of Guanting Reservoir during flood and dry seasons. This study provides technical means for the follow-up pollution control and sustainable improvement of river ecological environment.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3361-3370"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530049","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":"[Review on the Safety of Heavy Metals During Land Utilization of Municipal Sludge after Composting].","authors":"Yu-Yu Xu, Zi-Chen Zhang, Long-Jie Wen, Bai-Ju Yang, Lei Hou, Bing Zhang, Tan Chen, Ting Yang","doi":"10.13227/j.hjkx.202405051","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405051","url":null,"abstract":"<p><p>The leaching and release of heavy metals in municipal sludge pose critical constraints on the land utilization of municipal sludge after composting. To comprehensively understand the safety of heavy metals during land utilization of municipal sludge after composting， in this work， the heavy metal content levels of typical municipal sludge in China were summarized； the changes in the total amount， valence， and speciation forms of different types of heavy metals before and after composting were analyzed； and the short-term and long-term environmental behavior patterns of heavy metals after the application of municipal sludge composting products were reviewed. The mean concentrations of As， Cd， Cr， Cu， Hg， Ni， Pb， and Zn in municipal sludge in China were 21.78， 2.41， 108.57， 231.09， 2.35， 67.57， 55.03， and 630.24 mg·kg^-1， respectively， which remained within the permissible limits outlined in the China national standard GB 4284-2018 for Class A sludge products and exhibited a declining trend over the past three decades. During the composting process， the heavy metal content in the non-screened municipal sludge compost products decreased due to mixing auxiliary materials. Moreover， the humification of organic matter served to enhance heavy metal chelation， facilitating their conversion into more stable forms and thereby curtailing their mobility， leachability， and biological availability. The land utilization of municipal sludge compost products can gradually elevate the heavy metal content in soil， necessitating a judicious consideration of both application volumes and the duration of continuous usage to uphold soil quality and agricultural product safety. The heavy metals in municipal sludge compost products may undergo leaching and migration during land utilization， while literatures suggest minimal risks of this process to environmental quality， human health， crop yields， and soil fertility. Owing to variations in municipal sludge characteristics， the long-term safety implications of heavy metals in the land utilization of municipal sludge compost products warrant ongoing scrutiny； therefore， adjustments to the duration of continuous application should be mandated in accordance with specific sludge properties and land utilization conditions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3923-3933"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530077","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 and Driving Mechanisms of Supply and Demand Matching of Water Supply Services in the Yangtze River Economic Belt Based on the Perspective of Service Flow].","authors":"Yun Huang, Jing Liu, Bo-Fu Zheng, Liu-Jie He, Shu-Yang Wu, Ji-Hong Zhang, Han Liang, Zhi-Jian Wu, Jin-Qi Zhu, Wei Wan","doi":"10.13227/j.hjkx.202406084","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406084","url":null,"abstract":"<p><p>Water supply service is the basis of human survival and development， and mastering the dynamic balance of water resources is important for regional water resources management and high-quality development. Taking the Yangtze River Economic Belt as the study area， we use the InVEST model and the socio-economic development perspective to quantitatively assess the supply and demand of water supply services. Utilizing the ESDR and <i>Z</i>-score indexes， we reveal the spatial matching relationship of these services. Additionally， we construct a water supply service flow model by combining the D8 flow method and depth-first search. Through hotspot analysis and service flow spatial pattern， we categorize the study area into supply area， connection area， and demand area. We explore the influencing factors affecting the matching relationship between supply and demand of water supply service in each area from three perspectives： natural and social. The results show that： ① The water supply of the Yangtze River Economic Belt increased from 910 billion m³ in 2000 to 1 030 billion m³ in 2020， and the demand first rose and then fell. ② The ESDR of water supply service rose from 0.17 to 0.24， and the deficit range continued to contract. The spatial relationship between supply and demand was dominated by HL-type spatial mismatch clusters， and HL-type and LH-type spatial mismatch clusters transferred to HH-type and LL-type spatial match clusters. ③ The water supply service flow in the Yangtze River Economic Belt took the water system river network as the main flow path. The negative proportions of the flow in 2000， 2005， and 2010 were 1.2%， 1.7%， and 3.7%， respectively， and in 2015 and 2020 they were 2.2% and 1.3%， respectively， with the negative proportion increasing and then decreasing. ④ The dominant factor affecting the supply-demand matching relationship in the supply zone was precipitation （<i>q</i> = 0.44）， the contribution of the influence factors in the connectivity zone were all low， and the dominant factor affecting the demand zone was the share of construction land （<i>q</i> = 0.29）. The interaction between precipitation and other influencing factors was stronger in the supply and connection zones， while the interaction between socio-economic category factors was significantly stronger in the demand zone. The results of the study can provide scientific references for the management of water resources in the Yangtze River Economic Belt and the ecological compensation mechanism of the basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3693-3707"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530003","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":"[Influence of Straw Return and Fertilizer Matching on Microbial Quantity and Stoichiometric Characteristics and Microbial Entropy of Winter Wheat Soils].","authors":"Jian-Heng Song, Jing-Yu Huang, Xin-Yu Guo, Jia-Jie Song, Yu-Ming Huang, Chen-Yu Wang, Yong-Zhong Feng, Guang-Xin Ren, Xing Wang","doi":"10.13227/j.hjkx.202405157","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405157","url":null,"abstract":"&lt;p&gt;&lt;p&gt;In order to investigate the effects of the interaction between straw return and chemical fertilizer on soil microbial activity， microbial biomass （SMB）， soil microbial entropy （qmb）， and soil-microbial stoichiometric imbalance in agricultural soil so as to realize the stabilization of soil and increase the production of fertilizers， the experiment was carried out in a two-factor split-area design， with the primary treatments of straw return （W1） and straw non-return （W0） and the secondary treatments of no fertilizer， nitrogen fertilizer （N） and nitrogen-phosphorus fertilizer （NP）， and nitrogen-phosphorus fertilizer （NP）， to investigate the characteristics of soil-microbial carbon， nitrogen， and phosphorus changes and the coupling relationship between stoichiometric characteristics， stoichiometric imbalance， and microbial entropy under different treatments. The results showed that the soil organic carbon （SOC） content of W1NP was significantly increased compared with that of W0 （&lt;i&gt;P&lt;/i&gt;&lt;0.05）， the soil total nitrogen （TN） content of W1NP was increased by 56.56% compared with that of W0， there was no significant difference in the soil TP content （&lt;i&gt;P&lt;/i&gt;&gt;0.05）， and the C/P was significantly increased in the W1NP treatment compared with that in W0 （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. The soil microbial carbon （MBC）， soil microbial nitrogen （MBN）， and soil microbial phosphorus （MBP） contents were significantly higher （&lt;i&gt;P&lt;/i&gt;&lt;0.05） compared to those of W0. Soil MBN content was significantly higher in all treatments of straw return compared to that of W0 （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. The difference in soil MBP content between the W0NP and W1NP treatments was not significant （&lt;i&gt;P&lt;/i&gt;&gt;0.05）， but both were significantly higher than those of the other treatments （&lt;i&gt;P&lt;/i&gt;&lt;0.05）， and MBN/MBP was significantly higher than that of W0 in all treatments （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. Application of phosphorus fertilizer significantly increased qmbc， qmbn， and qmbp （&lt;i&gt;P&lt;/i&gt;&lt;0.05）， and the carbon and phosphorus imbalance ratios （C/P&lt;sub&gt;imb&lt;/sub&gt;） of the W1 and W0 treatments were not significantly different （&lt;i&gt;P&lt;/i&gt;&gt;0.05） but were significantly higher than in the other treatments （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. The nitrogen phosphorus imbalance ratio （N/P&lt;sub&gt;imb&lt;/sub&gt;） was significantly lower （&lt;i&gt;P&lt;/i&gt;&lt;0.05） in all treatments compared to that in W0. Correlation analysis showed that there was a significant positive correlation between MBC， MBN， and MBP （&lt;i&gt;P&lt;/i&gt;&lt;0.05） and all were significantly negatively correlated with stoichiometric imbalance （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. Redundancy analysis showed that the closest relationship was between MBC/MBP and qmbc， a significant relationship was between MBN/MBP and microbial mass entropy phosphorus， and the closest relationship was between microbial mass entropy nitrogen and MBN/MBP （&lt;i&gt;P&lt;/i&gt;&lt;0.05）. In conclusion， the straw return with the nitrogen and phosphorus fertilizer （W1NP） treatment was optimal for improving soil nutrients and","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3957-3964"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530060","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":"[Review of Machine Learning in Air Pollution Research].","authors":"Tong Sha, Liang-Qing Li, Shu-Qi Yan, Si-Yu Yang, Yang Li, Zi-Peng Dong, Qing-Cai Chen","doi":"10.13227/j.hjkx.202405208","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405208","url":null,"abstract":"<p><p>Air pollution is one of the most serious global challenges at present， and it has great practical importance to study and improve air quality. Traditional research methods mostly rely on multi-source observations and numerical models constructed based on atmospheric physics and chemistry theories， although these methods are limited in terms of accuracy， spatial and temporal coverage， and computational resources. As a powerful data processing and information mining tool， machine learning has begun to be applied by researchers in the field of air pollution research， aiming to reveal the changing patterns and influencing factors of air pollution through analyzing large amounts of data and predict future trends in air quality. This study reviews the typical applications of machine learning in air pollution research in recent years， mainly involving the following four aspects： inverting and estimation， monitoring， and prediction of atmospheric composition based on satellite remote sensing； improvement of air quality simulation and forecast accuracy； analysis of air pollution causes； and fusion of multi-source data. In addition， the scientific problems and technical difficulties in the current research are further discussed. Future research should focus on how to combine machine learning with traditional numerical models， such as developing intelligent parameterization schemes and learning model parameters. The application of machine learning in pollution source analysis， air quality health impact assessment， and multi-source data fusion techniques should also be explored to achieve more accurate air quality management and policy making.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3315-3328"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530076","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":"[Soil Health Evaluation of Non-grain Cultivated Land Based on Soil Quality Index Area Method and Soil Sensitivity-resistance Method： A Case Study of Ningbo City].","authors":"Ya-Ting Yang, Fang-Jin Qin, Shuang Wang, Cong Wang, Zhen-Ke Zhu, Gang Li, Shu-Quan Jin, Ti-da Ge","doi":"10.13227/j.hjkx.202406021","DOIUrl":"https://doi.org/10.13227/j.hjkx.202406021","url":null,"abstract":"<p><p>The aim of this study was to understand the soil health status of non-grain cultivated land in Ningbo， identify the main obstacle factors of non-grain cultivated land， explore the effect of non-grain on soil quality， and construct a comprehensive soil health evaluation system of non-grain cultivated land in Ningbo. Taking the soil of control cultivated land and typical non-grain cultivated land in Ningbo as the research object， 268 surface soil samples of 0-20 cm were collected under the utilization of control cultivated land， orchards， and nursery-grown plants. The 22 indexes of soil physics， chemistry， biology， and heavy metals were determined； the minimum data set was constructed by using principal component analysis， correlation analysis， and Norm value； and the soil health evaluation system in Ningbo was constructed using the soil quality index area method and soil sensitivity-resistance method， combined with soil multifunctionality to judge the quality of non-grain cultivated land in Ningbo. The results showed that： ① Compared with the soil of control cultivated land， soil bulk density （BD） in orchards and nursery-grown plants increased significantly （<i>P</i>&lt;0.05）， while the contents of organic carbon （SOC）， total nitrogen （TN）， available phosphorus （AP）， available copper （ACu）， soil extracellular enzyme activity， and soil microbial biomass carbon （MBC） decreased significantly （<i>P</i>&lt;0.05）. ② The minimum data set for soil health evaluation of non-grain cultivated land in Ningbo consisted of BD， electrical conductivity （EC）， SOC， dissolved organic carbon （DOC）， tammonium nitrogen （NH₄⁺-N）， TN， available iron （AFe）， available manganese （AMn）， and acid phosphatase （Phos）. The minimum data set verified by the total data set could reflect the effective information of non-grain soil quality in Ningbo. ③ Based on the soil quality index area method， the content of SOC， TN， AFe， AMn， and Phos in the cultivated land under non-grain utilization was lower than that of the control cultivated land. ④ According to the sensitivity and resistance analysis， the soil sensitive indexes included SOC， DOC， NH₄⁺-N， TN， AFe， and AMn， and the resistance index was BD and EC. ⑤ The random forest model showed that TN， SOC， <i>N</i>-acetyl-<i>β</i>-D-glucosidase （NAG）， and Phos were the main driving factors of soil multifunctionality index， while AFe， AMn， Phos， and DOC were the main driving factors of soil quality index. On the whole， the soil quality index and soil multifunctionality index of the two non-grain planting methods were lower than those of the control cultivated soil. Therefore， this study determined the health status of non-grain soil in Ningbo， identified the main obstacle factors of non-grain cultivated land， and provided some data support and theoretical basis for the classification and renovation of non-grain cultivated land and ensuring food security in Ningbo.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 6","pages":"3877-3889"},"PeriodicalIF":0.0,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530078","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}