工业工程最新文献

Inner lakes in irrigation districts serve as critical hydrological nodes for water-salt dynamics in arid and semi-arid regions, with their hydrochemical signatures providing sensitive indicators of regional water quality. This study comprehensively investigated the hydrochemical characteristics and controlling mechanisms of nine representative inner lakes in Ningxia's Qingtongxia Irrigation District under three recharge regimes (ecological water replenishment, irrigation return flows, and mixed sources). Combining hydrochemical analysis (Piper and Gibbs diagrams, ionic ratios) with principal component analysis (PCA), key findings revealed that: ① Inner lake waters were weakly alkaline (mean pH of 8.9 ±0.3) and brackish [mean total dissolved solids (TDS) concentration of (1.3±0.7) g·L^-1], dominated by Na⁺ (51%) and SO₄^2- (46%). Pronounced seasonal variations were observed, with summer irrigation period (July) showing 20% and 50% higher ionic mass concentrations than spring (May) and post-autumn (October) periods, respectively. ② Recharge regimes significantly influenced hydrochemical characteristics of inner lakes. Comparative analysis revealed that lakes fed by irrigation return flows and mixed sources showed significantly higher TDS (P<0.05) relative to ecological-water replenished lakes. The total ionic mass concentration in irrigation-fed lakes was approximately double that observed in ecological-water replenished lakes. Within the irrigation district, SO₄·Cl-Na water types predominated in the inner lakes recharged by irrigation return flows and mixed sources, while ecological-water replenished lakes displayed mixed Cl-Ca·Mg and SO₄·Cl-Na water types. ③ Both natural processes (evaporation-crystallization and rock weathering) and anthropogenic activities drove the hydrochemical evolution of the lake water. The anthropogenic activities were quantitatively assessed through principal component analysis, revealing that human activities accounted for 77.0% of the observed hydrochemical variations. The major anthropogenic sources were identified as: pesticide application/livestock and poultry farming/industrial wastewater (33.1%), nitrogen fertilizer/pesticide application (20.4%), potassium fertilizer application (12.3%), and ecological water replenishment (11.2%). These findings demonstrate that human activities predominantly control lacustrine hydrochemical variability within the irrigation district, providing a scientific basis for precision water-salt management and ecosystem rehabilitation in irrigated areas.

Aquatic communities serve as critical indicators for evaluating the health of aquatic ecosystems. However, challenges such as technical complexity and high costs in the collection and identification of aquatic organisms hinder assessment efficiency. This study proposes a deep learning-based predictive model for water quality indices to enhance evaluation timeliness and universality. From autumn 2020 to summer 2021, four aquatic ecological surveys were conducted at 16 monitoring points in the Beijing section of the Yongding River, identifying 118 macroinvertebrate species, 159 zooplankton species, and 107 phytoplankton species. By integrating multi-group biodiversity, the comprehensive ecological water quality index (CEWI) was constructed, revealing an overall water quality status of β-moderate pollution in the study area. Canonical Correspondence Analysis (CCA) identified water temperature (WT), pH, flow velocity (CV), water depth (WD), and dissolved oxygen (DO) as key environmental drivers of community structure. A BP neural network model was developed to predict the CEWI index, achieving an overall R² of 0.978, a Mean Square Error (MSE) of 0.106, and a Mean Absolute Error (MAE) of 0.262, thereby validating the model's effectiveness. Through the "environmental factor-biological response-model prediction" framework, this study provides data-driven insights and methodological innovations for ecological restoration in the Yongding River Basin, demonstrating significant practical value and regional guidance significance.

Microbial elemental utilization strategies play a crucial role in regulating soil organic carbon accumulation. However, the mechanisms of microbial metabolic limitations and elemental utilization efficiency in saline soils are unclear, limiting our understanding of how microorganisms in saline soils participate in organic carbon and nutrient cycling processes. Therefore, this study employed enzyme stoichiometry and ecological stoichiometric models to analyze the effect of microbial metabolic characteristics in agricultural and natural soils with salinity in the coastal region of eastern China. This study compared the microbial metabolic characteristics and elemental utilization efficiency in low-salinity and high-salinity soils. Based on this, we explored the contributing factors of microbial elemental utilization efficiency under different salinity conditions, integrating soil physical, chemical, and microbial properties. The results indicated that compared to those in low-salinity soils, microbial carbon and phosphorus limitations significantly increased in high-salinity soils, while microbial carbon and phosphorus utilization efficiencies decreased. In contrast, compared to those in natural soils, microbial carbon and phosphorus limitations significantly decreased in agricultural soils, leading to increased microbial carbon and phosphorus utilization efficiencies. Microbial carbon and phosphorus utilization efficiencies were influenced by available organic carbon and available phosphorus in agricultural soils, whereas microbial carbon and phosphorus limitations impacted them in natural soils, respectively. Under high-salinity conditions, soil chemical properties had the most significant effect on microbial carbon and phosphorus utilization efficiencies, while under low-salinity conditions, microbial properties and soil chemical properties were the primary influences on carbon and phosphorus utilization efficiencies, respectively. Structural equation modelling results indicated that microbial carbon limitation and phosphorus utilization efficiency were the two key factors regulating microbial carbon utilization efficiency. In summary, compared to in low-salinity soils, microbial metabolic limitations increased, and elemental utilization efficiencies decreased in high-salinity soils in the coastal region of eastern China. Therefore, revealing the patterns of microbial elemental utilization and their key influencing factors under different salinity conditions is of significant theoretical importance for guiding organic carbon accumulation and fertility enhancement in saline soils.

The optimization of vegetation pattern is of great significance for ensuring ecosystem function and sustainable water resource utilization. This study focused on the Xiliao River Basin, wherein an optimization analysis of vegetation pattern was conducted from the perspective of coordinating and stabilizing hydrological and ecological function stability, aiming to determine the appropriate scale and distribution of vegetation. Firstly, methods such as the InVEST model, morphological spatial pattern analysis, and circuit theory were employed to identify the key ecological restoration areas that support stable ecosystem services. Then, based on groundwater table zoning and terrestrial water storage recovery targets, the scale of cultivated land and grassland required to maintain terrestrial water balance was determined. Finally, considering both ecological and hydrological functions, the spatial distribution and scale of converting cultivated land to grassland in each district and county were determined, obtaining the optimized vegetation pattern for the entire basin. The results indicate that: ① From 1980 to 2020, the average value of habitat quality in the Xiliao River Basin decreased from 0.46 to 0.41, with 95.4% of the area experiencing varying degrees of degradation. To maintain the stability of basin ecosystem services, the key area for ecological restoration was 1 031.78 km², with cultivated land accounting for nearly 50%. ② Among the 24 districts and counties involved in the basin, 21 of them showed a deficit in terrestrial water storage during 1980-2020. To maintain hydrological function stability, the annual water consumption of the whole basin should be reduced by 427.829 million m³, and 4 278.29 km² of cultivated land should to be converted to grassland. ③ For Horqin Right Middle Banner, Tongyu County, Zhalot Banner, and Horqin Left Middle Banner, converting cultivated land within the key ecological restoration areas into grassland could ensure hydrological and ecological function stability. For the other districts and counties, converting cultivated land within the key ecological restoration areas into grassland could only achieve the stability of ecological service, but it still cannot meet the demand for hydrological function stability. Therefore, an additional 3 790.60 km² of cultivated land would still need to be reduced. This study addresses the limitations of a single perspective focused on hydrological or ecological functions. The findings can provide valuable references for formulating vegetation pattern optimization strategies and ensuring the stability of ecosystems and water resources.

In the face of the challenges of global climate change and environmental governance, the coordinated promotion of carbon reduction, pollution reduction, green expansion, and economic growth is of great significance for promoting the construction of ecological civilization, achieving high-quality economic development and high-level protection of the ecological environment. Based on TOE theoretical framework, 30 provinces in China from 2008 to 2023 were used as research samples, and the modified coupling coordination degree model and panel dynamic QCA method were used to analyze the internal relationship and coordination mechanism between carbon reduction, pollution reduction, green expansion, and economic growth in China. The results showed that: ① The coupling synergies of carbon reduction, pollution reduction, green expansion, and economic growth in China showed an overall increasing trend from 2008 to 2023, with the highest degree of synergies in the eastern region, followed by the central, northeast, and western regions, forming a spatial differentiation pattern of "higher in the eastern region and lower in the central and western regions". ② A single antecedent condition cannot constitute the necessary conditions for the high-coupling collaborative development of carbon reduction, pollution reduction, green expansion, and economic growth, but the necessity of enterprise governance empowerment showed an increasing trend year by year, reflecting the characteristics of time effect. ③ The interaction and matching of various elements produced six high-coupling collaborative configuration paths, which could be classified into three modes: technology-driven, structure-social oriented, and system collaborative. Among them, technology-driven was the core element to realize the high-coupling collaboration of carbon reduction, pollution reduction, green expansion, and economic growth. ④ In terms of time, the configuration based on technical competence and technology application efficiency showed an obvious upward trend. In terms of spatial distribution, the cases that could be explained by the configuration based on technical competence and social participation and cooperation showed obvious regional differences and were more distributed in Northeast China. Exploring the configuration path of coordinated development of carbon reduction, pollution reduction, green expansion, and economic growth is conducive to promoting the formation of a green, low-carbon, circular, and sustainable development model and provides theoretical support and practical guidance for formulating scientific and reasonable green transformation policies.

The low-carbon transformation of the logistics industry is an important component of achieving China's "dual carbon" goals. Analyzing the dynamic evolution of carbon emissions in China's logistics industry and exploring effective paths for green and low-carbon development under the background of digital intelligence are of great significance for the long-term development of the logistics industry. Based on the carbon emission coefficient method, we calculated the carbon emissions of the logistics industry in 30 provinces and cities in China and analyzed the dynamic evolution of the logistics industry using kernel density analysis. We also constructed a measurement index system for the level of digitalization and incorporated it into the TOE framework, based on the three levels of technology, organization, and environment, to construct the antecedents of carbon emissions in the logistics industry, using the fsQCA method for configuration analysis of carbon emission reduction pathways in the logistics industry. The results indicate that: ① The carbon emissions of China's logistics industry showed a trend of first increasing and then decreasing, with a gradual decrease in the concentration of carbon emissions in the early stage and an increase in regional differences in carbon emissions, indicating spatial polarization. The gap between regions gradually narrowed in the later stage, and the distribution became more concentrated and balanced. ② There were three driving modes for low-carbon emissions in the logistics industry: digital intelligence-open collaborative type, technology-market synergy type, and digital intelligence-environment collaborative type. Among them, digital intelligence-open collaborative type was the most common. ③ There were three driving modes for non-low-carbon emissions in the logistics industry: open deficiency type, technology-open deficiency type, and numerical intelligence deficiency type. This was mainly due to the lack of two variables, namely the level of digital intelligence and the degree of openness to the outside world. ④ The presence of multiple configurational paths indicated that the level of digital intelligence was a core condition for the low-carbon development of China's logistics industry. Empowering the low-carbon transformation of the logistics industry with digital intelligence is a way to alleviate the burden of high-quality development in the logistics industry. The research findings can provide important reference and guidance for the government and relevant departments.

Enhancing agricultural carbon productivity under the dual-carbon goal is important for mitigating global climate change, and the development of digital technology provides a new impetus for the low-carbon transformation of agriculture and the green development of the economy. Using the panel data of 30 provinces in China from 2013 to 2023, the projection pursuit model based on the accelerated genetic algorithm and the emission coefficient method are used to measure the development levels of rural digital technology and agricultural carbon productivity, respectively, and the panel fixed-effect model and spatial Durbin model are used to study the impact of digital technology development on agricultural carbon productivity. The results showed that: ① The development of digital technology in China had a spatial distribution pattern of decreasing gradient from east to west, and the agricultural carbon productivity in the southwest region had a high-high clustering pattern, with both showing an increasing trend year by year. ② The development of rural digital technology had a significant effect on the improvement of agricultural carbon productivity. ③ The heterogeneity test showed that the effect of digital technology on the improvement of agricultural carbon productivity was "western > central > eastern" and "balanced grain production and marketing area > main grain production area > main grain marketing area." ④The development of digital technology had a positive spatial spillover effect on agricultural carbon productivity in neighboring regions, with intensive spillovers within 300 km and detectable effects extending to 700 km of the spillover effect.

In practice, the synergistic effect of reducing pollution and carbon emissions in the manufacturing industry still faces significant non-coordinated contradictions. Analyzing and strengthening the driving mechanism of synergistic efficiency in reducing carbon and pollution in the manufacturing industry is of great significance for promoting high-quality development of the manufacturing industry in a coordinated manner. Based on panel data of segmented manufacturing industries from 2011 to 2022, we identify the temporal evolution characteristics of non-coordinated coupling in carbon reduction, pollution reduction, and efficiency improvement in different industries and analyze their dynamic mechanisms through the XGBoost-SHAP model, which is conducive to accelerating the achievement of the "dual carbon" goal in the region. The results indicate that: ① The carbon reduction efficiency and pollution reduction efficiency of the manufacturing industry showed a phased improvement feature, but the differences within the industry were gradually widening. Among them, the carbon reduction efficiency has maintained steady growth, while the improvement of pollution reduction efficiency is relatively slow and faces greater challenges. ② Most manufacturing industries have maintained or improved a low-level non-coordinated coupling state in terms of carbon reduction, pollution reduction, and efficiency improvement, reflecting relatively good balance and progress characteristics. However, the non-coordinated coupling in industries such as chemical fibers, non-ferrous and black metal smelting, and rolling processing, as well as petroleum and coal, were showing an upward trend. ③ Ownership structure and environmental costs were key factors leading to the non-coordinated coupling of carbon reduction, pollution reduction, and efficiency improvement in the manufacturing industry, with the impact of ownership structure being the most significant. Although R&D intensity showed a negative effect, it is crucial for improving efficiency. ④ The tripartite synergy mechanism formed by high R&D intensity, appropriate market competition, and optimized capital allocation can effectively promote the coordinated and coupled development of carbon reduction, pollution reduction, and efficiency improvement in the manufacturing industry. Especially in fixed-asset intensive industries, the improvement of technological level has become the key to breaking through environmental cost constraints and overcoming technological substitution resistance.

Alzheimer's disease (AD) is a common neurodegenerative disease. The usage of its therapeutic drugs is increasing with the intensification of population aging. These drugs cannot be completely metabolized in the human body and enter wastewater systems in the form of the originals or metabolites. In-depth investigation of the occurrence characteristics of these drugs in wastewater is of great significance for effective control and management. The concentrations and associated factors of four main AD drugs (donepezil, rivastigmine, galantamine, and memantine) and their metabolites in the influent of 210 wastewater treatment plants across 31 Chinese provinces were analyzed. The results indicated that detection rates of the above drugs in wastewater were high, with concentrations ranging from 7.47-21.60 ng·L^-1. Among them, the concentration of donepezil was significantly higher in East China and the Northwest and Northeast regions than that in Central China; the concentrations of rivastigmine and galantamine in Southwest China were significantly higher than those in East China; and the concentrations of memantine in Northwest and North China were significantly higher than those in East China. The above results indicated that the occurrence of these drugs showed a significant regional difference. Further, the AD drugs and metabolites with detection rates above 90% and excretion rates exceeding 20% (donepezil, rivastigmine metabolite, galantamine metabolite, and memantine) were chosen as biomarkers to evaluate AD prevalence. The prevalence of AD in different regions was estimated by wastewater-based epidemiology method, and the results were highly consistent with official statistical data. These results showed that the concentrations of AD drugs in wastewater influent were closely related to the AD prevalence. Additionally, correlation analysis also found that socioeconomic factors (such as stress, aging population, level of economic development, and health care services) had a significant positive correlation with the AD prevalence, indicating that socioeconomic factors may influence the occurrence of AD drugs in wastewater by affecting the AD prevalence. These results provide a scientific basis for further understanding of the characteristics of AD drugs in wastewater treatment plants and the development of corresponding control measures.