环境科学最新文献

{"title":"[Effects of Biochar on the Physicochemical Properties of Wheat Contaminated by Polyethylene Microplastics].","authors":"Fu-Peng Chen, Zi-Yi Li, Yun-Ting Dong, Pei-Yu Luo, Yue Chen, Di Lin, Lan-Tao Li","doi":"10.13227/j.hjkx.202506276","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506276","url":null,"abstract":"<p><p>The aim of this study was to investigate the physiological and ecological effects and mechanisms of corn straw biochar （CSB） on polyethylene microplastics （PE-MPs） contamination in wheat-soil systems， identify the main controlling factors， and thus to provide insights for remediating farmland microplastic pollution. A wheat soil pot experiment was conducted to investigate the effects and remediation efficiency of CSB （0.0%， 0.5%， 1.0%， and 3.0%） on the growth and development， photothermal physiology， leaf antioxidant enzyme activity， and soil enzyme activity of wheat under PE-MPs （0.0%， 0.5%， 1.0%， and 5.0%， particle sizes 10 μm， 100 μm） pollution. Meanwhile， a partial least square （PLS） regression model was used to quantitatively analyze the relationships between various physicochemical properties and the fresh weight， dry weight， and growth indices of winter wheat， thereby identifying key controlling factors. The results indicate that PE-MPs contamination （both particle sizes） exhibited inhibitory effects on wheat growth， development， and physicochemical properties， while CSB application effectively mitigated these negative stress impacts. Compared to that in the PE-MPs 5.0% × CSB 0.0% treatment， CSB amendment increased plant height， leaf area， and aboveground biomass of wheat by averages of 11.70%， 21.92%， and 44.63%， respectively. Single PE-MPs contamination significantly increased canopy temperature while reducing leaf thickness and photosynthetic efficiency in wheat， whereas CSB amendment demonstrated significant remediation and mitigation effects. Furthermore， the finer polyethylene microplastics （10 μm PE-MPs） demonstrated significantly stronger inhibition across the measured physiological parameters compared to that of coarser fractions （100 μm）. CSB exhibited superior remediation efficacy in finer particle treatments， with 10 μm PE-MPs primarily impairing photochemical activity （quantified by <i>F</i><sub>v</sub>/<i>F</i><sub>0</sub>， maxim μm quant μm efficiency of PSII）， while 100 μm particles predominantly influenced net photosynthetic rate （<i>P</i><sub>n</sub>） regulation. PE-MPs contamination significantly enhanced the activities of superoxide dismutase （SOD）， catalase （CAT）， and peroxidase （POD） in wheat leaves while reducing soil urease （S-URE）， acid phosphatase （S-ACP）， and dehydrogenase （S-DHA） activities. After the addition of CSB， leaf enzyme activities decreased to varying degrees， while soil enzyme activities were promoted to different extents. CSB exhibited significantly higher restoration efficiency in 10 μm PE-MP-contaminated soils. PLS results revealed that under co-exposure of CSB and PE-MPs， the key controlling factors for fresh weight and dry weight changes in wheat were： <i>F</i><sub>v</sub>/<i>F</i><sub><sub>0</sub></sub> （10 μm PE-MPs） for fresh weight， net photosynthetic rate （<i>P</i><sub>n</sub>） （100 μm PE-MPs） for fresh weight， and <i>F</i><sub>v</sub>/<i>F</i><sub><sub>0</sub></sub> （100 μm PE-MPs） fo","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5744-5760"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867073","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":"[Current Status and Prospects of Machine Learning Applications in Terrestrial Ecosystem Carbon Sink Modeling].","authors":"Xin-Yu Xie, Dong-Bin Wang, Yu Lian, Ren-Zong Jiao, Dan-Dan Li, Lei Duan, Ke-Bin He, Jing-Kun Jiang","doi":"10.13227/j.hjkx.202506358","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506358","url":null,"abstract":"<p><p>Under the \"Dual-Carbon\" strategy， assessing the spatial distribution of carbon sinks in typical regions represented by terrestrial ecosystems is essential for accurately characterizing regional carbon sink functions. Net ecosystem exchange （NEE） serves as a key indicator of ecosystem carbon fluxes. To accurately simulate the spatial patterns of regional NEE， commonly used methods include the inventory method， process-based models， and atmospheric inversion techniques. In recent years， machine learning has emerged as a powerful data-driven approach for spatial NEE modeling， offering distinct advantages in capturing complex nonlinear relationships. This study reviews the main methods applied in NEE spatial distribution modeling， elaborates on the principles and current applications of machine learning-based approaches， analyzes key feature selection and algorithm choices across different ecosystem types， and discusses the potential of applying machine learning to investigate NEE spatial characteristics in complex environments such as urban ecosystems.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5021-5032"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867117","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":"[Factors Decomposition and Carbon Reduction Potential of Domestic Waste Carbon Emission in the Yangtze River Economic Belt at the Urban Scale].","authors":"Yu-Zhu Chen, Ming Gao","doi":"10.13227/j.hjkx.202506299","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506299","url":null,"abstract":"<p><p>Promoting carbon emissions reduction of urban domestic waste is an important measure to achieve the \"dual carbon\" goals and achieve coordinated pollution reduction and carbon emission reduction. Based on the panel data of 111 cities above prefecture level in the Yangtze River Economic Belt from 2010 to 2020， this study calculated the carbon emissions of urban domestic waste， including the \"source reduction + terminal disposal\" process. The cities were clustered into four types based on population and economic characteristics， and the influencing factors of carbon emissions from urban domestic waste were decomposed. Furthermore， the BP neural network model and scenario analysis method were used to predict the carbon reduction potential of domestic waste in each type of city. The results show that： ① The total carbon emissions of domestic waste in the Yangtze River Economic Belt had been increasing year by year during the observation period. From the perspective of the carbon emission structure， landfill was always the main carbon source with the largest proportion， followed by incineration， and the proportion of biodegradation emissions was the lowest， while the growth rate of carbon reduction from recycling was the highest， and the reduction efficiency was continuously enhanced. ② The impact effects of treatment structure， economic development， urbanization level， and total population size on the carbon emissions of domestic waste were all positive， while the impact effects of waste generation intensity and industrial structure optimization were negative， and they showed different effects in various cities. ③ All types of cities had great carbon reduction potential in domestic waste， but there were great differences. The carbon reduction potential of the second type of city was far higher than that of other cities， while the first type of city had the lowest carbon reduction potential overall. The change degree of carbon reduction potential of the third and fourth types of cities was similar， showing a trend of increasing first and then decreasing. All localities should formulate and implement carbon reduction measures for domestic waste disposal according to local conditions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5179-5192"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867196","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 Influencing Factors of Ozone Concentration in Xi'an Based on Explainable Machine Learning].","authors":"Lei Huang, Xin-Hao Chen, Qing-Mei Bai, Yi-Dian Yang, Yi-Ge Wang, Hao-Hou Wu, Yu-Xing Gao","doi":"10.13227/j.hjkx.202506272","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506272","url":null,"abstract":"<p><p>Based on the atmospheric pollutant and meteorological monitoring data of Xi'an from 2020 to 2024， this study uses machine learning models such as XGBoost and combines with the SHAP interpretability algorithm to quantitatively analyze the key influencing factors of ozone （O₃） concentration. The results showed that the XGBoost model had excellent prediction performance， with a determination coefficient （<i>R</i>2） of 0.92， mean absolute error （MAE） of 9.52 μg·m^-3， and root mean square error （RMSE） of 13.19 μg·m^-3. Meanwhile， its prediction deviation for high-concentration O₃ （&gt;160 μg·m^-3） was significantly lower than that of other models. Analysis via the SHAP model indicated that meteorological factors played a dominant role in O₃ formation， contributing 61.7%， among which temperature， relative humidity， solar radiation， and sea level pressure were the main driving factors. Atmospheric pollutants contributed 39.3%， with nitrogen dioxide （NO₂） accounting for 73.8%. Seasonal differences in the contribution of various factors to O₃ formation showed that high temperature dominated in summer （temperature contributed 36.4%）， while NO₂ made the main contribution in winter due to increased heating emissions， with a contribution rate of 30.2%. The temperature， solar radiation， and PM_2.5 showed non-linear positive correlations with O₃， while NO₂ and relative humidity showed non-linear negative correlations with O₃. When the temperature &gt;20℃， the NO₂ concentration &lt;17 μg·m^-3， relative humidity &lt;65%， solar radiation &gt;1×10⁵ J·m^-2， and PM_2.5 concentration was in the range of 100-200 μg·m^-3， and the positive contribution to O₃ formation was significant. During the daytime， solar radiation and temperature synergistically activated photochemical reactions， NO₂ underwent photolysis to produce precursors， and these processes together drove the increase in O₃ concentration. This process corresponded to the \"high-contribution interval\" in SHAP scatter plots and the \"peak period\" in diurnal variation charts. In contrast， the positive contribution of PM_2.5 to O₃ was relatively significant around midday （11：00-15：00）， while it shifted to a negative impact during other time periods.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5821-5830"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148866967","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 and Prediction of the Zero-carbon Park Initiative in Anhui Jieshou High-tech Industrial Development Zone].","authors":"Dun Niu, Zi-Mu Xu, Lei Nie, Dan-Zhou Wu, Xin Pan, Shu-Heng Hu","doi":"10.13227/j.hjkx.202505295","DOIUrl":"https://doi.org/10.13227/j.hjkx.202505295","url":null,"abstract":"<p><p>Taking the Anhui Jieshou High-Tech Industrial Development Zone as the research object， we analyzed the current carbon emissions of the park in the past five years （2019-2023）， combining the logarithmic Mean Di Index （LMDI） model to analyze the driving factors， and forecasting emission trajectories under three scenarios （baseline S1， policy-driven S2， and enhanced mitigation S3） via the Long-range Energy Alternatives Planning System （LEAP） model. The results showed that the total carbon emissions of the park in the past five years have still been in the growth stage， mainly due to power consumption （accounting for 84.48%） and high energy-consuming industries （such as non-ferrous metal smelting， electrical machinery manufacturing， etc.）. The economic development effect was the core driving force for the growth of carbon emissions， while energy intensity and structural optimization showed inhibitory effects. LEAP's forecast indicated that under the Enhanced Emission Reduction Scenario （S3）， industrial parks could achieve carbon peaking （574，600 tons） by 2030 and approach carbon neutrality around 2040. Under the benchmark scenario （S1）， carbon emissions continued to increase， failing to achieve the \"dual carbon\" goals. Finally， tailored to the park's features， the study proposes recommendations establishing a theoretical framework and actionable pathways for zero-carbon industrial parks， while offering references for the low-carbon transition of similar industrial parks.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5366-5374"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867012","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":"[Spatiotemporal Evolution Characteristics and Driving Factors of the Decoupling Effect Between Carbon Emissions and Economic Growth at the Provincial Level in China].","authors":"Jian Li, Chen-Xu Wang, Xi-Xian Li","doi":"10.13227/j.hjkx.202506043","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506043","url":null,"abstract":"<p><p>In the context of unbalanced regional development posing severe challenges to the coordinated advancement of the \"dual carbon\" goals， it is of critical scientific significance to explore the spatio-temporal evolution characteristics and driving factors of carbon emission and economic growth decoupling at the provincial level in China. Based on panel data from 30 provincial regions from 2003 to 2022， this study systematically analyzes the spatio-temporal evolution characteristics and influencing factors by integrating the Tapio decoupling model， exploratory spatio-temporal data analysis （ESTDA）， and LMDI decomposition model. The results show： ① The carbon decoupling status exhibited a significant regional gradient pattern of \"eastern leading， central and western regions catching up.\" ② The carbon decoupling effect in provinces showed significant spatial dependence， with 83.3% of provinces exhibiting path dependence characteristics， and a prominent spatial club convergence effect forming between neighboring provinces. ③ Economic growth was the most essential positive driving factor for the increase in carbon emissions， with a cumulative contribution of 531 843 tons from the east， central， and western regions. The reduction of energy intensity was the primary negative inhibiting factor， with a cumulative contribution of -320 516 tons from the east， central， and western regions. Carbon emission intensity had turned into a significant negative suppression in the eastern region， while showing fluctuating positive driving in the central and western regions. The suppressive effect of industrial structure optimization primarily appeared in the later stage in the eastern region， and its impact on carbon emissions， along with population size， showed significant regional heterogeneity. ④ The efforts towards decoupling showed phase evolution and regional gradient differentiation； improvements in energy intensity were the only continuous positive factor promoting decoupling. This research provides important scientific evidence for formulating precise and coordinated regional emission reduction strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5126-5139"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867083","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":"[Assessment and Scenario Prediction of Pollution-carbon Reduction Synergies Toward Dual Carbon Goals in Shanxi Province].","authors":"Xing-Jian Wang, Hong Zhang, Xiao-Yu Zhang, Chao Su, Wen-Lei Chen","doi":"10.13227/j.hjkx.202507070","DOIUrl":"https://doi.org/10.13227/j.hjkx.202507070","url":null,"abstract":"<p><p>Shanxi Province is a typical resource-based region and a major energy hub in China. Facing critical challenges of high carbon emissions and air pollution， this province urgently needs to advance synergistic control of pollution-carbon reduction. Utilizing the low emissions analysis platform （LEAP） with energy consumption as the system boundary and 2020 as the base year， this study predicts energy consumption， greenhouse gas （GHG）， and air pollutant emissions from 2021 to 2060 under three scenarios： baseline， low-carbon， and carbon neutrality. The predicted CO₂ emissions for 2021-2023 were cross-validated against the actual CO₂ emissions from energy consumption to verify the accuracy of the model. Subsequently， based on these predictions， it employed decoupling analysis， the synergy elasticity coefficient of pollution-carbon reduction， and the LMDI model to quantitatively assess synergistic effects and driving mechanisms. The results indicate that the baseline scenario failed to achieve the carbon peak target， with air pollutant emissions projected to increase until approximately 2045. In contrast， the low-carbon and carbon neutrality scenarios would achieve carbon peaking in 2029 （534.78 ×10⁶ t） and 2025 （492.45×10⁶ t）， respectively. Under the carbon neutrality scenario， GHG emissions were projected to drop below 200 ×10⁶ t by 2060， and atmospheric pollutant emissions would begin a sustained decline around 2030. GHG and air pollutant emissions exhibited correlated emission patterns and synergistic mitigation potential； compared to the baseline scenario， both optimized scenarios advanced synergistic control by 15-20 years， characterized by GHG-dominant synergy. Analysis of driving factors identified economic development as the primary driver （contributing 904.98%）， while energy structure optimization and energy intensity reduction were key constraints. Control measures aligned with dual-carbon objectives effectively facilitated synergistic governance. Industrial transformation， stricter environmental policies and controls， and optimization of the energy consumption structure will enable Shanxi Province to achieve carbon reduction and air quality improvement targets sooner.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5275-5285"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867108","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":"[Evolution of Ecological Security Pattern in the Economic Belt of the Northern Slope of Tianshan Mountains Under Future Climate Scenarios].","authors":"Xiao-Xin Huang, Hong-Qi Wu, Yong Fang, Rui Guo, Nan Cui, Yu Zhao, Gui-Ling Liu","doi":"10.13227/j.hjkx.202505238","DOIUrl":"https://doi.org/10.13227/j.hjkx.202505238","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Investigating and clarifying the mechanisms by which climate change influences ecological security patterns is an effective measure for maintaining ecological security. Based on this， the study region selected is the Tianshan North Slope Economic Belt， utilizing three typical climate scenarios from the sixth coupled model intercomparison project （CMIP6）： SSP1-1.9， SSP2-4.5， and SSP5-8.5. First， the patch-generated land use change simulation model （PLUS model） was used to simulate land use evolution under different climate scenarios by 2060. Based on this， the integrated assessment model for ecosystem services and trade-offs （InVEST model） was applied to assess four key ecosystem services： water production， soil conservation， habitat quality， and carbon storage. Additionally， morphological spatial pattern analysis （MSPA） and the patch importance index （DPC） were employed to identify ecological source. Next， the analytic hierarchy process （AHP） was used to determine the weights of seven indicators， including land use， topography， habitat quality， and climate factors， to construct an ecological resistance surface. Finally， ecological corridors were extracted based on circuit theory. The results indicate： ① Ecological source areas were spatially distributed with more in the south and fewer in the north， denser in the south and sparser in the north， concentrated in the forested and grassland areas north of the Tianshan Mountains. In 2020， the area was 76 807 square kilometers， and in the SSP245 scenario by 2060， it slightly decreased to 75 979 square kilometers， showing the highest stability. Under the SSP585 scenario， the source area decreased to 66 904 square kilometers， with the highest degree of fragmentation. Under the SSP119 scenario， it expanded to 80 031 square kilometers with the best connectivity. ② Under the SSP245 scenario， the total length of ecological corridors reached its maximum， increasing to 4 509 kilometers. Under the SSP585 scenario， degradation was severe， with the total length of corridors decreasing by 21.7%， while the number of corridors increased by 10， resulting in a fragmented ecological network characterized by \"increased quantity but decreased quality.\" Under the SSP119 scenario， the total length of corridors was 3 592 kilometers， a slight increase of 5.4%， with the number of corridors decreasing to 81. The distribution structure of ecological corridors was optimized， with the most significant improvement in connectivity， highlighting the effectiveness of ecological conservation efforts. ③ Ecological pinch points increased in all three scenarios， with the most dramatic increase occurring in the SSP585 scenario， in which the number of hotspots increased by 235%， and the area increased by 15.91 square kilometers. The area of ecological barriers decreased in all three scenarios but remained large in the SSP585 scenario. Through SSP scenario comparisons， it was found that under the low-carbon scenario， source areas and ","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5636-5650"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867124","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 Microplastics on Growth Characteristics and Rhizosphere Soil Enzymatic Activity of Maize].","authors":"Cheng-Jiao Duan, Dan-Ning Yang, Zhi-Qin Zhang, Qiang Wang","doi":"10.13227/j.hjkx.202504006","DOIUrl":"https://doi.org/10.13227/j.hjkx.202504006","url":null,"abstract":"<p><p>Microplastics （MPs）， as emerging global pollutants， may disrupt agricultural ecosystems and affect crop growth and development. To clarify the impact of MPs on the soil-maize system， this study investigated the effects of traditional non-degradable MPs [polyethylene （PE） and polyvinyl chloride （PVC）] and biodegradable MPs [polylactic acid （PLA）] on rhizosphere soil nutrient content， organic acids， enzyme activity， as well as maize height and biomass. The results showed that the addition of conventional MPs reduced rhizosphere soil total nitrogen （TN）， total phosphorus （TP）， dissolved organic carbon （DOC）， and available phosphorus （AP） content； decreased extracellular enzyme activities related to carbon， nitrogen， and phosphorus cycling； and inhibited the shoot and root biomass of maize. Compared with that in the control （CK）， the aboveground biomass decreased by 33.29% and 22.86% after the addition of PE-MPs and PVC-MPs， respectively， while the belowground biomass decreased by 44.96% and 27.94%. In contrast， the addition of biodegradable PLA-MPs significantly increased rhizosphere soil organic carbon， TN， TP， DOC， and AP content； enhanced rhizosphere organic acid levels and extracellular enzyme activities related to soil carbon and phosphorus cycling； and reduced rhizosphere pH. Notably， PLA-MPs exhibited no significant inhibitory effects on maize height or biomass. Furthermore， Mantel tests revealed that the key factors influencing maize growth were rhizosphere soil TN， organic acid content， and cellulase activity （<i>P&lt;</i>0.05， <i>R</i>&lt;0.4）. These findings provide a data foundation and theoretical basis for evaluating the effects of conventional and biodegradable MPs on plant growth and soil ecological environmental responses.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5761-5769"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867149","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":"[Spatiotemporal Evolution， Trade-Offs and Synergies， and Driving Factors of Ecosystem Services in Jiziwan of the Yellow River].","authors":"Jing Cheng, Rui Chen, Peng Wang, Feng Hu","doi":"10.13227/j.hjkx.202506228","DOIUrl":"https://doi.org/10.13227/j.hjkx.202506228","url":null,"abstract":"<p><p>A comprehensive understanding of the spatiotemporal dynamics， trade-offs and synergies， and driving mechanisms of ecosystem services in the Jiziwan of the Yellow River is crucial for achieving synergy between ecological protection and high-quality development. This study evaluated five key ecosystem services-water yield， soil retention， carbon storage， food production， and wind erosion control-across the period 2001-2020 using the InVEST model， the normalized difference vegetation index （NDVI）， and the revised wind erosion equation. An integrated ecosystem services supply index （ESSI） was constructed to characterize the overall spatiotemporal evolution of ecosystem functions. Spearman's rank correlation coefficient was employed to analyze the trade-offs and synergies among ecosystem services， while partial least squares regression was used to explore the bidirectional influence of natural and anthropogenic factors on ESSI. The results show that： ① From 2001 to 2020， overall ecosystem service levels improved. Water yield increased from 5.97 to 12.5 billion m³， soil retention rose from 434 million tons to 596 million tons， carbon storage remained stable above 4.20 billion tons， and food production increased from 3.33 to 10.8 million tons. In contrast， wind erosion control declined markedly from 73 to 40 million tons， indicating enhanced provisioning and regulating services but a significant decrease in sand fixation capacity. ② Synergistic relationships dominated among most services， especially between water yield， soil retention， and carbon storage （Spearman coefficients consistently higher than 0.85）. The correlation between food production and carbon storage was weak （average value of only 0.199 9）. Notable spatial differences emerged at the prefectural level. For example， Wuhai City exhibited a synergistic relationship （<i>r</i>_s=0.192） between carbon sequestration and wind erosion control， while Yulin City showed a trade-off （<i>r</i>_s =-0.076）. ③ ESSI dynamics were jointly driven by climate change （precipitation density and temperature）， vegetation cover （NDVI）， and human activities （population density and nighttime light index）， with precipitation and NDVI acting as major positive drivers， while the intensity of human activities exerted negative impacts on some services. These findings provide a scientific basis for optimizing ecosystem service allocation and implementing differentiated ecological governance in the Jiziwan of the Yellow River.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 8","pages":"5534-5548"},"PeriodicalIF":0.0,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867259","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}