工业工程最新文献

The evaluation of ecological environment quality and the analysis of the causes of ecological change are important aspects of regional ecological management. In this study, based on the factors of greenness (NDVI), humidity (WET), heat (LST), and dryness (NDBSI), the salinity index (SI) was introduced to build an improved ecological remote sensing index (MRSEI). The spatial and temporal distribution pattern and driving mechanism of eco-environmental quality in the Beibu Gulf port area from 2000 to 2024 were analyzed. The results showed as follows: ① From 2000 to 2024, the overall ecological environment quality in the study area showed a slow improvement trend, and the MRSEI grade was mainly in the middle level, with the average annual value ranging from 0.25 to 0.68, showing a spatial distribution pattern of high in the west and low in the east. ② There was a strong spatial autocorrelation of ecological environment quality in the study area. The spatial aggregation patterns were mainly H-H and L-L. The H-H gathering area was mainly forest land and mountain, and the L-L gathering area was mainly agricultural land and construction land. ③ In 2000-2024, the area of ecological environment quality improvement was significantly larger than the area of degradation, and the area of no significant improvement and significant degradation was the most extensive. The future change trend is mainly future degradation. ④ The ecological environment quality in the study area was influenced by both natural and human factors. Among them, the average annual temperature had the strongest explanatory power, followed by evapotranspiration, slope, distance to artificial surface, and NPP. The interaction of all factors increased to a certain extent, and the interaction effect of average annual temperature and evapotranspiration was the strongest.

Accurately quantifying the spatial distribution of topsoil pH and identifying its influencing factors is essential for recognizing potential land-use challenges and promoting the recovery and balance of soil ecological functions. In this study, 1 795 soil samples were collected from the hilly region of southern Sichuan, China, to model and analyze topsoil pH using four base machine learning models: random forest (RF), support vector regression (SVR), extreme gradient boosting (XGB), and neural network (ANN), as well as two ensemble learning approaches: Boosting and Stacking. Model performance was assessed and compared, and Shapley additive explanations (SHAP) were applied to interpret the contribution and interaction of environmental predictors. The results showed that ensemble models achieved higher predictive accuracy than individual base learners, with the Boosting model yielding the best performance (R²=0.862). All six models demonstrated consistent spatial prediction trends, though a slight compression in value range was observed between predicted and measured pH values. Soil pH across the study area displayed a spatially stratified pattern, generally decreasing from north to south. The four most influential factors were TK, BD, SOC, and annual precipitation. Partial dependence analysis indicated that soil pH increased significantly when TK ranged from 16.25 to 17.34 g·kg^-1 but decreased once TK exceeded 17.83 g·kg^-1. SOC exhibited a negative effect on soil pH, particularly when SOC content was greater than 8.25 g·kg^-1. Moreover, interaction analysis revealed heterogeneity in the synergistic effects among various factors. These findings highlight the potential of interpretable ensemble learning methods for modeling soil properties and provide theoretical support for developing targeted strategies to regulate soil pH. They also offer a scientific basis for improving soil health resilience and advancing sustainable soil ecological management in complex agricultural landscapes.

Digitization and greening are the preferred paths for the current development of new quality productivity. Accordingly, by constructing indicators of digitization, greening, and new quality productivity, we analyze the coordination degree of digitization and greening coupling and its promotion mechanism for the improvement of new productivity in 30 provinces in China from 2012-2022 by using the modified coupling, benchmark regression, mediating effect, moderating effect, and threshold effect models. The results showed that: First, the overall coupling coordination of digitization and greening was not high, but it was gradually increasing, and there was an obvious clustering effect, with "Beijing-Shanghai-Guangdong" dominating in the east, "Hubei" dominating in the center, and "Sichuan-Chongqing" dominating in the west, manifesting itself in the form of an "eastern leading, central lagging, rise of the west" pattern. Second, in promoting the development of new quality productivity, the coupling effect of digitization and greening showed a significant positive contribution in different regions, with the most significant contribution in the eastern region, followed by that in the central region, and relatively weaker in the western region. Further mechanism analysis revealed that science and technology innovation played an important intermediary role in promoting the development of new productivity through the coupling effect of digitization and greening, which was further enhanced by the increase in the level of industrial agglomeration. In addition, the impact of environmental regulation on the coupling effect of digitization and greening showed an inverted "U" shape, and excessive environmental regulation may weaken its effect on the promotion of new productivity.

Based on long-term vegetation index and meteorological data from 2000 to 2022, this study analyzes the spatiotemporal changes in fractional vegetation coverage (FVC) in the economic belt on the northern slope of Tianshan Mountains and quantifies the impacts of climate change and human activities. Trend analysis, the Hurst index, the geographical detector method, and residual analysis are used to assess FVC variation and predict future trends. The results showed that: ① From 2000 to 2022, the FVC of the northern Tianshan economic belt exhibited a slow fluctuating upward trend, with an average annual growth rate of 1.2×10^-3 a^-1. The spatial distribution was heterogeneous, presenting a "high in the northwest-southeast axis and low around the edges" pattern, with low fractional vegetation coverage (FVC ≤ 0.2) being dominant, accounting for 62.45%. ② During the same period, both improvement and degradation trends coexisted, and the Hurst index analysis indicated that 51.87% of the region may face potential risks of vegetation degradation in the future. ③ The geographical detector analysis showed that land use was the most significant driving factor for FVC variation, with a q-value of 0.670, making land use one of the key factors influencing FVC change. ④ The relative contribution rates of climate change and human activities to the variation in fractional vegetation coverage were 15.54% and 84.46%, respectively. In conclusion, future ecological construction should focus on strengthening the role of human activities in promoting the increase of fractional vegetation coverage, while enhancing the monitoring and protection of existing vegetation to prevent degradation trends.

Henan Province plays a crucial strategic role in maintaining national food security. Exploring the collaborative evolution of water resources and cultivated land resources, as well as their driving factors, is of significant importance for achieving the goal of building up strength in agriculture. Using Henan Province as the study area, this study constructed an evaluation index system for the carrying capacity of water resources and cultivated land resources based on statistical data from 2005 to 2023. The entropy method was employed to determine the weights of the evaluation indices and quantitatively assess the carrying capacity levels. The Haken model was used to analyze the synergistic effect between the two resources, while a modified gravity model and social network analysis were applied to reveal the characteristics of the synergistic network. Additionally, the GeoDetector was employed to explore the driving factors of the collaborative relationship. The results indicate that: ① From 2005 to 2023, the water resource carrying capacity index of Henan Province increased by 0.123, rising from a low capacity level to a higher level, while the cultivated land resource carrying capacity index increased by 0.132, rising from low to high capacity, with Pingdingshan City still maintaining a general capacity level. ② From 2005 to 2023, the synergistic degree between water resource carrying capacity and cultivated land resource carrying capacity increased from 0.424 to 0.557, rising from low-level synergy to high-level synergy. Except for Pingdingshan City, which was at a medium-level synergy, all regions in the province achieved high-level or above synergy. ③ The synergistic effect between water resource carrying capacity and cultivated land resource carrying capacity in Henan Province had formed a complex, multi-threaded spatial network structure. During the study period, the stability and connectivity of the spatial network improved, with the intermediary roles of cities weakening, resource control becoming more decentralized, and the network becoming more balanced. The connections and interactions between regions became more significant. ④ Cultivated land resource carrying capacity, as a sequence parameter, determined the current level of water resource carrying capacity and dominated the path and direction of their synergy. Per capita water resources, residents' consumption level, agricultural electricity intensity, and per capita net income of rural residents were the core driving forces of the synergy between water and cultivated land resource carrying capacity, which were simultaneously influenced by multiple factors and interactions. The findings provide decision-making references for the collaborative evolution and dynamic adaptation of water and cultivated land resources in Henan Province.

To explore the health risks brought by the transfer of thallium (Tl) in farmland soil through the food chain to planted crops (taking sweet potatoes as an example), three areas with similar geographical environments and planting methods but different Tl contents were selected for sample collection. This study indicated that Lianguang Village and Gaozhai Village in Pinghe, Zhangzhou, belonged to the farmland surrounding the Huashanxi River Basin and had higher Tl levels, with Tl contents in the soil ranging from 0.413 to 0.700 mg·kg^-1. The Tl content in Xibian Village, Nanjing, Zhangzhou ranged from 0.283 to 0.337 mg·kg^-1, which was lower than the average concentration of 0.49 mg·kg^-1 in the Earth's crust. However, none of these soils exceeded the maximum limit specified for agricultural soil (1 mg·kg^-1). Tl content in sweet potatoes ranged from 0.004 45 to 0.032 9 mg·kg^-1, with an average of 0.015 9 mg·kg^-1. Tl content in sweet potatoes was far below the human safe consumption standard of 0.3 mg·kg^-1. An in vitro bionic gastrointestinal digestion and absorption method was employed to study the digestion and absorption effects of Tl and other trace metals after ingestion of sweet potatoes, which had absorbed Tl through the food chain from the soil. The bioaccessibility and bioavailability of Mg, K, Ca, Mn, Fe, Cu, Cr, Cd, and Pb in the chyme after gastrointestinal digestion of sweet potatoes were determined. There was a strong correlation between the Tl content in sweet potatoes and the bioaccessibility and bioavailability of K, Ca, and Fe (R²=0.956 4-0.995 3, P < 0.05), while no correlation was found between the Tl content and the bioaccessibility and bioavailability of Mg, Mn, Cu, Cr, Cd, and Pb. The results indicated that Tl could be transferred from farmland soil to cultivated crops, affecting human health through the food chain. Even low doses of Tl in food had a competitive inhibitory effect on the digestion and absorption of K, Ca, and Fe, leading to metabolic disturbances of trace elements. Therefore, measures must be taken to reduce the bioavailability of Tl in soil to mitigate its impact on human health.

It is of great significance for regional ecological construction to scientifically understand the spatial and temporal distribution of vegetation change and explore the differential response relationship between vegetation change and driving factors. Based on the normalized difference vegetation index (NDVI) data set and temperature and precipitation data set from 2000 to 2020, this study used Sen + MK trend test, Hurst index, and partial correlation analysis to analyze the time-varying law of vegetation in Haihe River Basin and the time-lag effect on different climatic factors. Combined with residual analysis, the influence mechanism of climate change and human activities on vegetation driving was discussed, and the contribution rate of the two to vegetation change was quantified. The results showed that: ① NDVI increased at a rate of 0.003 26 a^-1 from 2000 to 2020. The CV value was between 0 and 1.42, with an average of 0.07. The area with low fluctuation and low fluctuation of NDVI accounted for 79.73%, and the overall stability was good. The area with an upward trend of NDVI in the future accounted for 51.11%. ② The lag periods of NDVI response to various climatic factors were different. The lag periods of temperature and precipitation were 3 months and 1 month, respectively, and the maximum partial correlation coefficient of temperature was -0.68 to 0.82. The maximum partial correlation coefficient of precipitation was 0.07 to 0.92. ③ The relative contribution rates of human activities and climate change to vegetation change accounted for 45.69% and 54.31%, respectively. The results of this study can provide a scientific basis for vegetation restoration and protection in the Haihe River Basin.

Exogenous application of appropriate selenium (Se) can alleviate the stress of metal cations on plants. Arsenic (As) is an anionic metalloid, and the detoxification mechanism of Se on As in soil-plant systems remains unclear. Therefore, pak choi was selected as the experimental material. A pot experiment was conducted to investigate the effects of the co-application of exogenous Se and As on the growth, physiological metabolism, photosynthesis, uptake, and transport of Se and As in pak choi, as well as the transformation of Se and As fractions in the soil, aiming to reveal how exogenous Se alleviates As stress. The results showed that low Se treatment (0.5 mg·kg^-1) promoted pak choi growth under low and medium As treatment (30 mg·kg^-1 and 60 mg·kg^-1), though the differences were not significant (P≥0.05). In contrast, the co-application of high Se (2.5 mg·kg^-1) and high As (100 mg·kg^-1) significantly inhibited the growth of pak choi (P < 0.05). Under low and medium As stress, low Se treatment effectively alleviated the toxicity of As to pak choi. Compared with those in the treatment without Se application, the glutathione peroxidase activity, nitrate reductase activity, and photosynthesis (net photosynthetic rate, stomatal conductance, intercellular CO₂ concentration, transpiration rate, and SPAD value) of pak choi were significantly increased by 0.45%-32.53% (P < 0.05), while the electrolyte leakage, superoxide anion radical content, malondialdehyde content, and proline content of pak choi were significantly decreased by 6.47%-22.84% (P < 0.05). High Se and As co-application showed a synergistic toxic effect. In addition, compared with that in the treatment without Se application, the translocation factor value of As in pak choi under high Se treatment was significantly decreased by 27.95%-56.57% (P < 0.05), reducing the enrichment of As in the edible parts. The application of Se decreased the proportion of soluble As in soil by 0.1%-14.00% and increased the proportion of residual As by 2.28%-10.13% compared with that in the treatment without Se application, thus reducing the availability of As in soil. These findings demonstrate that 0.5 mg·kg^-1 Se mitigates low-medium As stress by enhancing plant physiology and immobilizing As in soil.

To reveal the spatiotemporal variation of antibiotic pollution in the Nandu River Basin, Hainan Province, and assess its ecological risk, a large-volume injection-high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was used to quantitatively analyze 44 antibiotics from five major categories. The risk quotient (RQ) and joint probability curves (JPCs) methods were employed for ecological risk assessment. The results showed that 10 antibiotics were detected in the Nandu River Basin, with total concentrations ranging from ND to 2 034.38 ng·L^-1. Sulfachloropyridazine had the highest concentration (ND-1 993 ng·L^-1), followed by sulfamethoxazole (ND-949.81 ng·L^-1) and florfenicol (ND-482.16 ng·L^-1). The mean antibiotic concentrations in different hydrological periods were as follows: normal water period (112.92 ng·L^-1) > dry season (78.29 ng·L^-1) > wet season (69.85 ng·L^-1). The RQ method indicated that sulfamethoxazole, trimethoprim, lincomycin, erythromycin, and clindamycin posed high risks, with risk quotients of 9.50, 7.59, 2.99, 2.43, and 1.34, respectively. The exceedance rates of the predicted no-effect concentration (PNEC) for these five antibiotics were 11.9%, 4.76%, 4.76%, 4.76%, and 2.38%, respectively. The JPCs-based assessment showed that erythromycin had the highest risk product (3.45%), indicating a moderate risk, while lincomycin had a maximum risk product of 0.67%, indicating a low risk. The risks of other antibiotics were negligible. The results of ecological risk assessment were influenced by antibiotic concentration, detection frequency, and toxic effects. By constructing a multi-tiered ecological risk assessment approach, this study scientifically defined ecological risk thresholds for antibiotics, effectively addressing the potential issues of underprotection or overprotection in traditional assessment methods. This provides a scientific basis for hierarchical management and spatially differentiated control of antibiotic pollution at the regional scale.

With the acceleration of urbanization and industrialization, the problem of urban ozone (O₃) pollution in China has become increasingly serious. Aiming to address the limitation that the traditional time series model predicts O₃ concentration without fully considering the stochastic factors, a machine learning fusion model, i.e., the integrated model of seasonal autoregressive integral sliding average (SARIMA) and back-propagation neural network (BPNN), is proposed. The model decomposes the data into linear and nonlinear parts and fully utilizes the linear fitting advantage of the SARIMA model and the nonlinear mapping ability of the BPNN in order to improve the prediction accuracy. Specifically, the seasonal trend decomposition method (STL) was firstly applied to the original O₃ series to extract its trend, seasonal components, and random effects, based on which a SARIMA model was built to predict the linear changes in O₃ concentration. Subsequently, the nonlinear part of the data was input into the BPNN to fit the stochastic fluctuations. Ultimately, the prediction results of the SARIMA and the BP model were integrated to obtain the comprehensive prediction output. The O₃ concentration monitoring data of Hefei City from 2021 to 2023 were selected to construct a combined SARIMA-BP neural network model. The results showed that the root mean square error (RMSE) reached 8.385 2 μg·m^-3, which improved the prediction accuracy by 55.88% and 22.39% compared to that of the single SARIMA and BP models, and it was better than the SARIMA-LSTM model prediction effect, providing a theoretical basis for urban ozone pollution prevention and control.