Search Results (15)

Using density functional theory (DFT) and the Gaussian 09 program, the study calculated Gibbs free energy to understand how easily each NP can transform. Results showed that only 2,6-dinitrophenol (2,6-DNP) and 2-chloro-6-nitrophenol (2-Cl-6-NP) had Gibbs free energies above 0 kJ/mol. The study also evaluated the toxicity of the NPs, leading to the identification of trinitrophenol (TNP), 2-chloro-4-nitrophenol (2-Cl-4-NP), and 2-nitrophenol (2-NP) with the highest risk scores. In the present study, binding energies were used only as comparative indicators of enzyme–substrate interaction favorability within a screening framework, rather than direct measures of catalytic degradation efficiency. The enzyme 1,2-dioxygenase from Acinetobacter baylyi ADP1 showed strong degradation effects on catechol, with significant binding energies for 2-NP, 2-Cl-4-NP, and TNP. The PS-AOP changed the degradation environment, which reduced enzymatic efficiency. The study also modified specific amino acids in enzymes to improve their performance. For example, the enzyme 1DLT-6 had a degradation increase of nearly 27% compared to the reference enzyme. Finally, we tried to measure the impact of different forces on the breakdown of nitrophenols by enzymes. We used a two-dimensional amino acid map based on enzyme–ligand interactions and a visualization of non-covalent interactions. Our findings show that van der Waals forces and electrostatic forces are the main factors affecting how well the material breaks down. From a sustainability perspective, the study highlights a promising strategy for mitigating secondary pollution, improving the environmental compatibility of PS-AOP-based remediation, and supporting safer and more sustainable restoration of petroleum hydrocarbon-contaminated soil and groundwater. These findings help strengthen the theoretical basis for developing greener post-oxidation remediation pathways. Full article

Like other agricultural products, food legumes production faces uncertainty risks stemming from climate change, which may affect yields and consequently impact farmers’ livelihoods. Agricultural insurance serves as one of the climate change adaptation measures available to farmers, helping mitigate the impacts of climate change on agricultural production and livelihoods. While considerable attention has been paid to climate change adaptation through production-side measures, comparatively fewer micro level studies examine insurance adoption as an adaptive response, particularly among food legume farmers. Based on a survey of 460 food legume farmers in Baicheng City, Jilin Province of China, this study employs a binary probit regression model to analyze the relationship between climate change perceptions and farmers’ adoption of agricultural insurance as an adaptation measure. Farmers’ climate change perception is measured through four indicators: perceived changes in average annual temperature, precipitation, drought severity, and frost severity over the past five years. Robustness tests are conducted by using a replacement econometric model, altering the climate change perception variable, and implementing sample restriction. Results indicate that food legume farmers’ perceptions of climate change exhibits significant correlation with their agricultural insurance purchasing behavior. Farmers who perceive lower temperatures and more severe frosts are more inclined to purchase agricultural insurance. Participation in food legume production cooperatives and prior experience with yield reductions exert significant positive correlation with insurance purchase decisions. Therefore, enhancing targeted outreach and education, leveraging the role of cooperatives in insurance promotion, and implementing differentiated insurance promotion based on disaster experiences hold positive implications for reducing farmers’ exposure to climate change risks. The findings further offer valuable insights into climate adaptation policy in other drought-prone, legume-growing regions. Full article

The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSSP) represents a core challenge in modern manufacturing: achieving synergistic optimization of multiple conflicting objectives while pursuing production efficiency and energy sustainability. To address this, this study proposes an enhanced hybrid heuristic algorithm—KNN–Tabu Search NSGA-II (KTNSGA-II)—for simultaneously optimizing completion time, machine load, and total energy consumption. First, a three-objective mathematical model is established. Subsequently, four key strategies are integrated: (1) workload balancing initialization rapidly generates high-quality initial solutions; (2) an adaptive job-level crossover mechanism dynamically adjusts subset sizes during iterations to balance global exploration and local exploitation; (3) K-nearest neighbor-based congestion distance calculation maintains population diversity; (4) tabu search applied to non-dominated solutions on the Pareto front for local refinement. Extensive experiments on standard benchmark instances demonstrate that KTNSGA-II significantly outperforms representative algorithms in terms of convergence and diversity. For large-scale Behnke benchmark instances, KTNSGA-II achieves an average hypervolume (HV) improvement of 32.32% compared to other comparison algorithms. Furthermore, this method substantially enhances solution diversity: the Spacing Performance (SP) metric improved by 39.72%, indicating more uniform distribution of Pareto optimal solutions; the Diversity Metric (DM) increased by 57.54%, reflecting broader coverage and more even distribution along the Pareto frontier boundary. These results confirm that KTNSGA-II generates higher-quality, better-distributed Pareto fronts, achieving a more optimal trade-off between completion time, machine load, and energy consumption. Full article

As an important ecosystem service, water conservation is influenced by land use related to human activities. In this study, we first evaluated spatial and temporal changes in water conservation in Baicheng City, western Jilin Province, from 2000 to 2020. Then, we identified three different scenarios: the natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). We coupled the Patch-generating Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models to predict the distribution of land use types and water conservation in Baicheng City under these scenarios for 2030. The results showed the following: (1) The average water conservation in Baicheng City from 2000 to 2020 was 7.08 mm. (2) Areas with higher water conservation were distributed in the northwest and northeast, while lower water conservation areas were distributed in the central and southwest of Baicheng City. (3) The simulation results of the future pattern of land use show an increasing water conservation trend in all three scenarios. Compared with the other two scenarios, the ecological protection scenario is the most suitable option for the current development planning of Baicheng City. Under the ecological protection scenario (EPS), ecological land is strictly protected, the area of agricultural land increases to some extent, and the overall structure of changes in land use becomes more rational. This study provides a reference for land resource allocation and ecosystem conservation. Full article

Heavy metal pollution in agricultural soils poses a serious threat to food security. Therefore, it is crucial to conduct risk assessments and issue early warnings about high levels of metal contamination for the sustained prosperity of agriculture. To assess the risks, identify the sources, quantify the amounts, and determine the extent of pollution from seven heavy metals, as well as to provide early warnings, 78 soil samples were collected from farmed lands in the Songnen Plain of Jilin Province. The average concentrations of Zn, Cu, Mn, Pb, Cd, Ni, and As were found to be 2.05, 1.5, 0.2, 1.09, 2.68, 1.53, and 1.17 times higher than the background values of Chinese soils, respectively. Source analysis indicated that toxic Pb originates from vehicle exhaust emission, while Cd, Cu, and Ni are attributed to industrial activities. Zn and As are likely associated with agricultural practices, and Mn predominantly stems from natural environmental sources. The geo-accumulation index suggests relatively high, accumulation levels for Zn, Cu, Mn, and Pb. Meanwhile, the single-factor pollution index indicates elevated pollution levels of Zn, Cu, and Cd. Potential ecological risk assessment reveals that certain areas within Changchun and Baicheng cities exhibit higher ecological risks. Notably, Cd has the highest potential ecological risk index (RI) of the seven heavy metals and warrants special attention. By analyzing annual pollutant accumulations, predictions can be made about the heavy metal content in four cities within the Songnen Plain, enabling the issuance of early warnings regarding soil heavy metal risks. The findings suggest that without proactive measures to mitigate heavy metal accumulation in soils, Changchun and Songyuan will reach severe pollution levels by 2031 and 2029, respectively. Full article

The feasibility of the two methodologies was confirmed to compare the results of determining mung bean origins using Raman and Near-Infrared (NIR) spectroscopy. Spectra from mung beans collected in Baicheng City, Jilin Province; Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province; and Sishui County, Shandong Province, China, were analyzed. We established a traceability model using Principal Component Analysis combined with the K-nearest neighbor method to compare the efficacy of these methods in discriminating the origins of the mung beans. The total cumulative variance explained by the first three principal components from the NIR of mung beans from different origins was 99.01%, which is 6.71% higher than that derived from Raman. Additionally, the discrimination rate for mung bean origins based on NIR spectral data reached 98.67%, outperforming the Raman-based approach by 22.67%. These findings indicate that NIR spectroscopy is more effective than Raman spectroscopy is in tracing the provenance of mung beans. Full article

Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process–Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and comparison of the disaster risk caused by two typhoons, Maysak and Haishen, in Jilin Province. Game theory enhances precision in evaluation beyond conventional approaches, effectively addressing the shortcomings of both subjective and objective weighting methods. Typhoon Maysak and Typhoon Haishen exhibit analogous tracks. They have successively exerted an impact on Jilin Province, and the phenomenon of overlapping rain areas is a crucial factor in triggering disasters. Typhoon Maysak features stronger wind force and greater hourly rainfall intensity, while Typhoon Haishen has a longer duration of rainfall. Additionally, Typhoon Maysak causes more severe disasters in Jilin Province. With regard to the four dimensions of disaster risk, the analysis of hazards reveals that the areas categorized as high risk and above in relation to the two typhoons are mainly located in the central-southern and eastern regions of Jilin Province. Typhoon Maysak has a slightly higher hazard level. During the exposure assessment, it was determined that the high-risk areas occupied 16% of the gross area of Jilin Province. It is mainly concentrated in three economically developed cities, as well as some large agricultural counties. In the context of vulnerability analysis, regions classified as high risk and above constitute 54% of the overall area. The areas classified as having high vulnerability are predominantly located in Yushu, Nong’an, and Songyuan. From the analysis of emergency response and recovery ability, Changchun has strong typhoon disaster prevention and reduction ability. This is proportional to the local level of economic development. The mountainous areas in the east and the regions to the west are comparatively weak. Finally, the comprehensive typhoon disaster risk zoning indicates that the zoning of the two typhoons is relatively comparable. When it comes to high-risk and above areas, Typhoon Maysak accounts for 38% of the total area, while Typhoon Haishen occupies 47%. The regions with low risk are predominantly found in Changchun, across the majority of Baicheng, and at the intersection of Baishan and Jilin. Upon comparing the disasters induced by two typhoons in Jilin Province, it was observed that the disasters caused by Typhoon Maysak were considerably more severe than those caused by Typhoon Haishen. This finding aligns with the intense wind and heavy rainfall brought by Typhoon Maysak. Full article

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000–4000 cm⁻¹) and preprocessing (Standardization, Savitzky–Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90–180–270–360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models. Full article

P2A (Power to ammonia) is one of the important ways of large-scale consumption of renewable energy, and one of the important technological routes for the chemical industry to realize low-carbon and clean development. The new off-grid energy power to hydrogen ammonia system lacks the support of large power grids due to the complex mathematical model of the system, more variables, and cumbersome constraints, which leads to model solving difficulties, and the production simulation results obtained suffer from the problems of low economic efficiency and high new energy power abandonment rate. To address the shortcomings of the algorithm, which converges slowly and easily falls into the local optimum when solving the model, this paper applies the Sparrow Search Algorithm (SSA) to the problem of economic optimization of new energy hydrogen synthesis and ammonia system scheduling. Firstly, based on the characteristics of wind and light, the operating characteristics of an electrolyzer, and the characteristics of an electrochemical energy storage device, and taking the economic optimization of the electric hydrogen synthesis ammonia system as the objective function, the economic optimization scheduling model of an off-grid new energy electric hydrogen synthesis ammonia system is established for 24 h production simulation. Secondly, the model is solved based on the sparrow search algorithm, and the speed of solving and the economic benefits of the system are analyzed in comparison with the conventional algorithm. Finally, the proposed off-grid wind-powered hydrogen synthesis ammonia system based on the sparrow search algorithm is verified to achieve the optimal operation of the 24 h production simulation through an actual example in the Daan area of Baicheng City, Jilin Province, which shows that the optimized system has better economic efficiency and the new energy is completely consumed, thus verifying the reasonableness and validity of the algorithm proposed in this article. Full article

Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m³/kW·h, with an EWCC greater than 7 observed in the Tao’er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area. Full article

Water is an important resource for crop production; identifying the spatial pattern of the crop water footprint (WF) is of great significance for the optimization of water resource consumption and management in agricultural production. This study quantified the green, blue and grey water footprints (GWF, BWF and GRWF) and water consumption (GWC, BWC and GRWC) of rice, maize and soybean at the 1 km grid level and city level in Northeast China in 2019 based on the CROPWAT 8.0 model. The results showed that the average total water footprints of rice (TWFr), maize (TWFm) and soybean (TWFs) were 624.31 m³·ton⁻¹, 527.26 m³·ton⁻¹ and 1298.21 m³·ton⁻¹, respectively. The spatial differences in the WF of each crop were obvious in Northeast China, with the highest values of TWFr mainly occurring in Baicheng, Dalian and Qitaihe; the highest TWFm values were mainly found in Baicheng, Yingkou and Hulundao, and the highest TWFs were mainly found in Baicheng, Chifeng and Tongliao. The total water consumption of all three crops (TWCc) in Northeast China was 94 billion m³·yr⁻¹ (42% green, 26% blue and 32% grey), in which the total water consumption of maize production (TWCm) accounted for 60%. The production of rice, maize and soybean in Northeast China mainly depends on green water, grey water and blue water, respectively. Combining the results of the spatial patterns of crop TWF and TWC, the study revealed that the planting pattern of crops in Northeast China was relatively reasonable for sustainable water use. Meanwhile, cities that have the potential to enhance crop production and cities that should improve their water use efficiency and reduce fertilizer application were also identified. Full article

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies. Full article

Water shortage, water pollution, shrinking water area and water mobility are the main contents of the water resources crisis, which are widespread in the social and economic development of Jilin Province. In this paper, a three-stage hybrid model integrating evaluation, prediction and regulation is constructed by combining the load-balance method and the system dynamics method. Using this model, the current states of water resources carrying capacity (WRCC) in 2017 and the trend of water demand/available from 2018 to 2030 were obtained. Using the orthogonal test method, the optimal combination program of agricultural and industrial water efficiency regulation and water resources allocation was selected. The results show that the pressure of the human–water resources system in Changchun, Liaoyuan and Baicheng is greater than the support, and the other six cities are not overloaded. The water demand in Jilin Province and its nine cities will increase from 2018 to 2030, if the current socio-economic development pattern is maintained. Therefore, we change the water quantity carrying capacity index by controlling agriculture, industrial water efficiency and trans-regional water transfer. Compared with 2015, among the optimal program obtained, the change range of the water use per 10,000 RMB of agricultural output is (−5%, 25%), and the water use per 10,000 RMB of industrial added value is (−45%, −35%), and the maximum water transfer is 1.5 billion m³ per year in 2030. This study analyzes the development pattern of WRCC in the process of water conservancy modernization in Jilin Province and provides reference for other provinces to make the similar plan. Full article

Droughts are among the more costly natural hazards, and drought risk analysis has become urgent for the proper planning and management of water resources in grassland ecosystems. We chose Songnen grassland as a case study, used a standardized precipitation evapotranspiration index (SPEI) to model drought characteristics, employed run theory to define the drought event, and chose copula functions to construct the joint distribution for drought variables. We applied two kinds of return periods to conduct a drought risk assessment. After evaluating and comparing several distribution functions, drought severity (DS) was best described by the generalized extreme value (GEV) distribution, whereas drought duration (DD) was best fitted by gamma distribution. The root mean square error (RMSE) and Akaike Information Criterion (AIC) goodness-of-fit measures to evaluate their performance, the best-performing copula is Frank copula to model the joint dependence structure for each drought variables. The results of the secondary return periods indicate that a higher risk of droughts occurs in Keshan county, Longjiang county, Qiqiha’er city, Taonan city, and Baicheng city. Furthermore, a relatively lower risk of drought was found in Bei’an city, Mingquan county, Qinggang county, and qian’an county, and also in the Changling county and Shuangliao city. According to the calculation of the secondary return periods, which considered all possible scenarios in our study, we found that the secondary return period may be the best indicator for evaluating grassland ecosystem drought risk management. Full article

The Momoge National Nature Reserve (MNNR) is located at the intersection of Nenjiang and Taoer Rivers in Baicheng City, Jilin Province, where the Taoer River is the main source of water for the nature reserve. However, due to the construction of the water control project in the upper reaches of the Taoer River, the MNNR has been in a state of water shortage for a long time. To guarantee the wetland function of the nature reserve, the government planned to carry out normal and flood water supply from Nenjiang River through the West Water Supply Project of Jilin Province. Therefore, how to improve the utilization of flood resources effectively has become one of the key issues of ecological compensation for the MNNR. In this paper, a flood resources optimal allocation model that is based on the interval two-stage stochastic programming method was constructed, and the corresponding flood resource availability in different flow scenarios of Nenjiang River were included in the total water resources to improve their utilization. The results showed that the proportion of flood resources that were used in the MNNR after optimization was more than 70% under different flow scenarios, among which the proportion of flood resources under a low-flow scenario reached 77%, which was 23% higher than the proposed increase. In addition, the ecological benefits of low, medium, and high flow levels reached the range of 26.30 (10⁶ CNY) to 32.14(10⁶ CNY), 28.21(10⁶ CNY) to 34.49(10⁶ CNY) and 29.41(10⁶ CNY) to 35.94(10⁶ CNY), respectively. According to the results, flood resources significantly reduce the utilization of normal water resources, which can be an effective supplement to the ecological compensation of nature reserves and provide a basis for the distribution of transit flood resources in other regions. Full article