工业工程最新文献

By constructing open-top warming chambers in an alpine swamp meadow in a permafrost region of the Qinghai-Xizang Plateau， three experimental groups were established： the control group （CK）， low warming group （T₁： 1.5-2.5℃）， and high warming group （T₂： 3-5℃）， to evaluate the effects of warming on rhizosphere soil enzyme activities and metabolites. The results indicated that rising temperatures significantly affected soil enzyme activities. Compared to that in CK， β-glucosidase activity was markedly increased in the T₁ group and further enhanced in the T₂ group； sucrase activity was significantly decreased in the T₁ group， whereas it exhibited a significant increase in the T₂ group； cellulase and polyphenol oxidase activities were notably decreased in the T₁ group； and ascorbate peroxidase activity was significantly reduced in the T₂ group. Metabolomics analysis revealed that amino acid metabolism and secondary metabolite accumulation were significantly promoted in the T₁ group. In contrast， the T₂ group showed a marked increase in lipid metabolism， with lipid metabolite abundance significantly higher than that in CK. Volcano plot and principal component analysis （PCA） results demonstrated that warming significantly altered soil metabolite composition and metabolic network structure. Pearson correlation analysis identified significant associations between metabolites and enzyme activities in the rhizosphere soil， such as a strong positive correlation between PA （16∶0/16∶0） and the activities of sucrase and β-glucosidase.

Exploring the spatio-temporal pattern， dynamic evolution， and carbon compensation zoning of the county land use carbon budget has great practical significance for territorial spatial pattern optimization and building a fair and effective regional carbon compensation mechanism in the Beijing-Tianjin-Hebei Region. Based on land use cover， nighttime light， and energy consumption data， a land use carbon budget estimation model was constructed in the Beijing-Tianjin-Hebei Region area. Based on the exploratory spatial data analysis method and kernel density estimation， the spatio-temporal pattern and dynamic evolution trend of carbon emissions were analyzed. The economic value of carbon offsetting was calculated based on the carbon offsetting value model. Finally， carbon compensation zoning was carried out based on K-means clustering combined with ecological function zoning. The results indicated that： ① The fitting R2 of the indirect carbon emission estimation model for construction land was 0.776 8， with good simulation accuracy and the estimation effect meeting the expected standards. ② The carbon sink in the Beijing-Tianjin-Hebei Region showed a continuous growth trend； the carbon source showed a decreasing trend after peaking； and the overall increase in net carbon emissions was consistent with the carbon source and exhibited significant positive spatial correlation， with four types of aggregation patterns. ③ The absolute difference in net carbon emissions between districts and counties in the Beijing-Tianjin-Hebei Region as a whole， Tianjin， and Hebei Province showed a trend of expansion， while the absolute difference in net carbon emissions between districts in Beijing shifted from expansion to a trend of narrowing. The tailing effect in the Beijing-Tianjin-Hebei region as a whole and the three provinces was quite significant. ④ In 2022， Xicheng and Dongcheng districts in Beijing and Heping district in Tianjin were key areas for payment. Chongli district in Zhangjiakou， Daxing district in Beijing， and Fengning Manchu Autonomous County in Chengde were key areas for compensation. The compensation areas-restricted development zones included 76 districts and counties， which was the type with the highest proportion， mainly concentrated in most districts and counties of the Yanshan-Taihang Mountain ecological conservation area and the Bashang Plateau ecological protection area in Hebei Province.

Environmental pollution and carbon emissions predominantly stem from fossil fuel consumption， sharing common sources and processes. This inherent co-production relationship establishes a scientific foundation for implementing coordinated governance to enhance the Synergistic Effect of Pollution Reduction and Carbon Reduction （SEPCR）， thereby accelerating socio-economic green transition. However， existing literature lacks comprehensive assessments of SEPCR at the urban level in China and seldom examines its influencing factors from a spatiotemporal heterogeneity perspective. To address this gap， this study comprehensively uses the SBM-DEA model with unexpected outputs and coupling coordination model to measure the SEPCR of cities in China. The spatial Markov chain and data visualization methods are used to analyze the spatiotemporal evolution pattern of SEPCR in Chinese cities from 2004 to 2022. The spatiotemporal geographic weighted regression model （GTWR）， kernel density curve， and map visualization methods are used to investigate the spatiotemporal heterogeneity of factors influencing the SEPCR. The study revealed that： ① The overall SEPCR was on the rise， with a fluctuating decline prior to 2014 before entering a rapid growth period. ② The SEPCR exhibited strong stability， with a gradual evolution process. SEPCR levels of neighboring cities were positively correlated with the upward transfer probability of the city， while negatively correlated with the downward transfer probability of the city. ③ The SEPCR showed a spatial differentiation pattern of northeast>east>west>central regions， with high-value clusters concentrating in eastern coastal and northeastern regions， while central and western regions were generally stable and emerging progress. Low-value areas centered in Shanxi Province showed spatial convergence. ④ The regression results of GTWR indicate that industrial structure， energy efficiency， and technological innovation had a promoting effect on the SEPCR， while population expansion， economic development， and energy structure mainly exhibited an inhibitory effect. Each influencing factor demonstrated significant spatiotemporal heterogeneity.

Elucidating the spatio-temporal evolution patterns and driving mechanisms of ecological environment quality （EEQ） in the Dabie Mountain region of northeastern Hubei provides critical scientific support for ecological security barrier construction in the middle Yangtze River Basin. This study develops a triadic analytical framework （pattern-driver-threshold） through the integration of multi-source remote sensing， meteorological， and socioeconomic data， systematically investigating EEQ dynamics from 2001 to 2021 via combined Theil-Sen Median trend analysis， Mann-Kendall testing， and Hurst index prediction. Key findings include：① An overall EEQ improvement trend occurred （51.95% of areas enhanced） despite cyclical fluctuations， with multi-year means evolving from 0.470 in 2001 to a peak of 0.514 before declining to 0.497 in 2021. ② There was pronounced spatial stratification showing "excellent/good" grades concentrated in northwestern， northeastern， and southwestern mountainous zones versus "poor" levels in central-southern river plains. ③ The optimal spatial scale for driver analysis was identified as 7.5 km， with land use intensity （exhibiting growing stress effects）， vegetation coverage， precipitation， elevation， temperature， and built-up area proportion constituting dominant drivers. EEQ demonstrated negative nonlinear responses to land use intensity， temperature， and built-up areas， contrasting with positive relationships to vegetation coverage， elevation， and precipitation， whereas socioeconomic factors （population density， GDP） showed negligible impacts. ④ Projections indicated persistent spatial polarization， with 39.72% of the region predicted for EEQ improvement against 30.75% degradation areas. These results provide quantitative foundations for dynamically optimizing ecological conservation boundaries， regulating construction land intensity， and formulating climate-resilient governance strategies， while establishing a transferable paradigm for sustainable mountain ecosystem management.

Nitrate is one of the most common contaminants in groundwater. It is of considerable significance to identify its sources for the prevention and control of groundwater pollution. Here， we took the groundwater of a typical area in Beijing plain as the research object， using the qualitative analysis of hydrochemical indexes， combined with stable isotope analysis in R （SIAR） and absolute principal component score-multiple linear regression model （APCS-MLR） to further identify and quantitatively analyze the contribution of different factors to NO₃^-. The results revealed that the main hydrochemical type of the groundwater in the study area was HCO₃-Ca·Mg， and the predominant anion and cation were HCO₃^- and Ca²⁺， respectively. The hydrochemical ions in groundwater mainly originated from the weathering of aquifer rocks but were also influenced by human activities. The results of SIAR demonstrated that the soil organic nitrogen was the most important source of NO₃^- in the groundwater， with a contribution rate of 43.2%， followed by chemical fertilizer with a contribution rate of 38.7%， and fecal sewage had a relatively small contribution. The results of APCS-MLR analysis indicated that the soil leaching caused by the rising groundwater level in the study area was the major driving factor to the increase in NO₃^- concentration in groundwater， with a contribution rate of 52.6%. Additionally， non-point source pollution caused by agricultural and living activities also affected the content of NO₃^- in groundwater， with contribution rates of 11.7% and 10.8%， respectively. The analysis results of hydrochemical indexes， SIAR， and APCS-MLR were consistent and complemented each other. Thus， the combination of multiple qualitative and quantitative statistical analysis methods can make it more accurate and effective in the identification of the groundwater nitrate sources.

In order to study the current situation and sources of heavy metal pollution in Zhangbei County， Bashang Grassland， 69 surface soil samples， 16 ancient weathering crust soil samples， and 35 rock samples were collected to test and analyze the contents of eight heavy metals such as Cd， Pb， Hg， Zn， Cu， As， Cr， and Ni. The enrichment factor method and potential ecological risk index method were used to study the characteristics of heavy metal enrichment and ecological risk assessment. Multivariate statistical analysis and the positive matrix factorization （PMF） model were combined to analyze the sources of heavy metals in the soil. The results showed that the average content of eight heavy metal elements in the soil of the study area was lower than the screening value for soil pollution risk in agricultural land and the average content of surface soil in Hebei Province. Only Cd showed slight enrichment in heavy metals， while the rest of the heavy metal elements were not enriched overall. The potential ecological risks of a single indicator were ranked from high to low as follows： Cd>Hg>Pb>As>Ni>Cu>Zn>Cr. The comprehensive index of potential ecological risks showed that the study area was mainly mild， with only two samples reaching a moderate risk level， and the main contributing factors were Cd and Hg. The results of multivariate statistical analysis and PMF model source analysis indicated that heavy metals in the soil of the study area were mainly controlled by the weathering of the parent rock. The high-value areas of Cr， Ni， Cu， Zn， and Cd were mainly controlled by the basalt parent rock， while the high-value areas of Pb and As were mainly controlled by parent rocks such as detrital rocks and granite. Hg was a composite pollution source of multiple factors such as coal burning， atmospheric dust deposition， and parent rock weathering. As， Cr， Pb， and Zn were also affected by industrial activities， agricultural activities， transportation， and household waste.

This study integrates multi-source datasets from 24 counties （cities， and districts） in the Henan section of the Yellow River Basin to construct a composite rural ecosystem health （REH） assessment framework encompassing "resource-environment-social-economy" dimensions. Leveraging spatial Gini coefficient analysis， dominant factor identification， and Geographic Detector modeling， the spatiotemporal evolution， spatial differentiation patterns， and driving mechanisms of REH were systematically investigated. The results showed that： ① The comprehensive REH index in the study area ranged from 0.284 5 to 0.590 1， indicating moderate overall health levels with progressive improvement trends. Spatially， a distinct west-high， central-stable， east-low gradient emerged， characterized by environmental and social subsystem advancements offset by declining resource and economic subsystem performance. ② REH classification identified four primary categories （healthy， sub-healthy， unhealthy， and pathological） and seven subcategories. Healthy-type areas （20.83%） clustered in the western region， sub-healthy zones （37.50%） dominated central areas， while unhealthy （33.33%） and pathological-type systems concentrated in northern/eastern regions， notably in Wuzhi and Wen County. ③ Ecosystem service value per unit area emerged as the strongest single explanatory factor. Notably， socioeconomic drivers exhibited increasing influence on REH dynamics in recent years， with interactive factor effects demonstrating significantly higher explanatory power than individual factors. In summary， differentiated regulatory measures are proposed based on the above results： The development of healthy counties should be prioritized and "characteristic industries+ecological protection" should be continuously promoted. Sub-healthy counties should focus on improving the quality of arable land and promoting agricultural tourism. Unhealthy counties must highlight "cultural protection+comprehensive improvement，" and pathological counties should aim to implement targeted ecological restoration projects and socioeconomic revitalization programs to address systemic vulnerabilities.

Achieving a balance between ecological conservation and economic development is essential for regional coordination and sustainable development. This study examines 56 national key ecological function counties （cities） in Sichuan Province， employing a coupling coordination model to assess the current state and dynamic evolution of ecological-economic coupling coordination. Temporal analysis， trend surface analysis， and spatial autocorrelation analysis are utilized to delineate spatiotemporal evolution patterns， while a geographically and temporally weighted regression （GTWR） model is applied to explore influencing factors and spatiotemporal heterogeneity. The results indicate that： ① The coupling coordination degree generally fell within the range of 0.4-0.5 （mild imbalance） and 0.5-0.6 （primary coordination）， exhibiting a U-shaped trend of initial decline followed by subsequent improvement. ② High coordination areas were mainly concentrated in the eastern and southern regions of Sichuan Province， with an expanding north-south disparity. ③ Spatial correlation of the coupling coordination degree weakened initially before strengthening， revealing significant spatial heterogeneity and regional clustering characteristics. ④ Key influencing factors， including river network density， road network density， government intervention， industrial structure， and population density， displayed pronounced spatiotemporal heterogeneity. These findings suggest that while the alignment between ecological protection policies and economic development strategies in Sichuan's national key ecological function areas is strengthening， regional disparities remain prominent. Therefore， targeted policies should be formulated based on local conditions to achieve a sustainable synergy between environmental conservation and economic growth.

At the historical intersection of innovation-driven development and the "two-carbon" strategy， clarifying the spatial relationship of carbon neutral technological innovation among cities in the Yellow River Basin is of great significance for promoting ecological protection and high-quality development in the Yellow River Basin. Based on the effective measurement of carbon neutral technology innovation efficiency of 77 prefecture-level cities in the Yellow River Basin from 2010 to 2022， this study comprehensively evaluates the spatial network correlation characteristics of carbon neutral technology innovation efficiency with the help of the modified gravity model and social network analysis and carries out β convergence analysis to explain its spatial convergence rule. The results showed that the carbon neutral technology innovation efficiency in the Yellow River Basin presented a pattern of "lower reaches > middle reaches > upper reaches"， with local dynamic characteristics and unbalanced characteristics co-existing. The network density of carbon neutral technology innovation efficiency in the Yellow River Basin gradually decreased from upstream to downstream and then to the middle reaches. The coastal cities in the lower reaches of the Yellow River Basin developed rapidly under the spillover effect of other plates. Zhengzhou， Jinan， and Heze have become important central cities to radiate and drive the development of the surrounding areas. The spatial convergence analysis showed that the convergence characteristics and catch-up effect of carbon neutral technological innovation efficiency in cities in the Yellow River Basin were significant， and the convergence speed was the fastest in the downstream area.