工业工程最新文献

To compare the environmental impact and carbon footprint of gray hydrogen, blue hydrogen, and green hydrogen, inventories were obtained through literature research. Some inventories that were not available in China were obtained through foreign inventories combined with localized power conversion. The localized end-point destructive life cycle impact assessment method was used to calculate the environmental impact potential of the raw material acquisition, transportation, and hydrogen production stages of five hydrogen products. The carbon footprint was calculated, and the sensitivity analysis and uncertainty analysis were carried out and compared with the ReCiPe method. The results showed that： ① The environmental impact from large to small was： gray hydrogen （coal） （1 203 mPt·kg^-1） > blue hydrogen （coal） （876 mPt·kg^-1） > gray hydrogen （gas） （492 mPt·kg^-1） > green hydrogen （323 mPt·kg^-1） > blue hydrogen （gas） （252 mPt·kg^-1）. The environmental impacts of gray hydrogen and blue hydrogen were mainly concentrated in climate change, fine particulate matter formation, and fossil fuels. The environmental impacts of green hydrogen were mainly concentrated in climate change, fine particulate matter formation, fossil fuels, and mineral resources. ② The carbon footprint from large to small was： gray hydrogen （coal） （23.79 kg·kg^-1, measured by CO₂eq, the same below） > blue hydrogen （coal） （11.07 kg·kg^-1） > gray hydrogen （gas） （10.97 kg·kg^-1） > blue hydrogen （gas） （3.47 kg·kg^-1） > green hydrogen （1.97 kg·kg^-1）. Direct carbon emissions in the production process of gray hydrogen and blue hydrogen accounted for the largest proportion, whereas that of green hydrogen accounted for a large proportion of power input. ③ Measures to reduce environmental impact and carbon emissions include reducing direct emissions of pollutants and greenhouse gases, reducing power consumption, and strengthening raw material substitution and reduction.

By constructing a land ecological evaluation index system at the village scale and using models such as spatial correlation analysis, hotspot analysis, and obstacle factor diagnosis, the basic characteristics, spatial differentiation, and obstacle factors of land ecological status in Jiangsu Province were studied. This study sought to clarify the foundation, structure, function, and benefit characteristics of land ecosystems and optimize land management and policy regulation. The results showed that： ① The spatial distribution of land ecological status in Jiangsu Province was high in the north and low in the south, with multiple high-value areas radiating outward and decreasing, with low value centers radiating outward and increasing. The distribution area of the highest and lower values was relatively small, whereas the area of the middle value area was the largest. The higher values were mainly distributed in the suburbs and edge areas of each county. ② The spatial autocorrelation of land ecological status in Jiangsu Province was significant, with hot spots mainly concentrated in northern Jiangsu and cold spots concentrated in southern Jiangsu, as well as some areas of Taizhou and Nantong. The spatial distribution of cold and hot spots showed a complementary pattern with the level of regional development. The comprehensive index value of land ecology in developed areas was lower, whereas the index value in underdeveloped areas was higher. ③ The natural background conditions of Class Ⅰ land ecological zone in Jiangsu Province were superior, with good ecological construction and benefits and a high level of ecological status. The obstacle factors mainly included the proportion of water bodies and the average annual degradation rate of forest land. The Class Ⅱ land ecological zone was mostly located in the Huainan region and mainly composed of plain landforms. The Class Ⅲ land ecological zone had the largest area, located in the riverside areas of southern Jiangsu. The obstacle factors mainly included the average annual degradation rate of arable land and the proportion of soil pollution area. By controlling land ecological risks, the early warning level of ecological crisis could be improved.

To explore the characteristics of phytoplankton communities and their relationship with environmental factors in different habitats of Hedi Reservoir, the inflow rivers, estuaries, and reservoir area of Hedi Reservoir were investigated in February （recession period）, April （flood period）, July （flood period）, and December （recession period） of 2022. During the investigation, 231 species of phytoplankton that belong to seven phyla were identified, and the cell density of phytoplankton ranged from 2.94 × 10⁶ - 8.04 × 10⁸ cells·L^-1. Phytoplankton cell density in flood periods were higher than that in recession periods, and that was higher in estuaries and the reservoir area than that in inflow rivers. Meanwhile, the cell density of phytoplankton in the estuarine and reservoir area was dominated by Cyanobacteria throughout the year, especially Raphidiopsis raciborskii, whereas the cell density of phytoplankton in inflow rivers was dominated by Cyanophyta, Chlorophyta, and Bacillariophyta. In the inflow river area, the dominant species of cyanobacteria were Microcystis aeruginosa, Limnothrix redekei, Pseudanabaena circinalis, and Merismopedia punctata； the dominant species of Chlorophyta were Chlorella vulgaris and Crucigenia tetrapedia； and the dominant species of Bacillariophyta were Chlorella vulgaris and Melosira granulate. The highest biodiversity （Shannon-Wiener Index, Pielou index, and Margalef index） were observed in the inflow river area of Hedi Reservoir. The correlation analysis （Pearson） indicated that the environmental factors that were significantly correlated to phytoplankton communities included water temperature, dissolved oxygen, pH, conductivity, nitrogen, and phosphorus concentration. The RDA analysis indicated that phytoplankton communities in the inflow river area were mainly affected by pH and total nitrogen concentration, which were majorly affected by water temperature and pH in the estuarine area and chiefly affected by turbidity and pH in the reservoir. The pH affected the changes in phytoplankton communities in all three different habitats, whereas the inflow river area was significantly affected by total nitrogen concentration, and the estuarine and reservoir were significantly affected by water temperature and turbidity, respectively.

An in-depth understanding of the soil nutrient status and balance relationship can help the effective recovery and management of alpine degraded meadows. In order to study the balance relationship among soil carbon, nitrogen, and phosphorus nutrients during the heavy degradation stage of meadows, field sampling and investigation, indoor analysis, and mathematical statistics were used to explore the characteristics and driving factors of changes in soil carbon, nitrogen, and phosphorus content, storage, and ecological stoichiometry during the heavy degradation stage of alpine meadows in the Sanjiangyuan region. The results showed that in the heavy degradation stage, miscellaneous grass plants occupied absolute dominance, soil C∶N∶P was approximately 32.83∶3.87∶0.67, and there was certain nitrogen limitation. The coefficients of variation of soil carbon, nitrogen, and phosphorus content were in the following order： organic carbon （1.09） > total nitrogen （0.63） > total phosphorus （0.29）. The organic carbon content and the carbon and nitrogen ratio showed a significant linear decreasing trend with the increase in the grassland degradation index （GDI）, while the total phosphorus content and organic carbon storage showed a significant non-linear change, in which the total phosphorus content showed a significant gentle U-shaped distribution, and the organic carbon storage decreased more gently at the beginning of the heavy degradation stage and then decreased sharply when the GDI was 57.9. The results of Mantel correlation analysis showed that the soil carbon to nitrogen ratio, carbon to phosphorus ratio, and nitrogen to phosphorus ratio showed significant correlation with organic carbon content and storage and total nitrogen storage. The results of structural equation modeling indicated that soil water content had direct effects as well as indirect through vegetation factors, soil carbon, nitrogen, and phosphorus ecological stoichiometry ratios, and soil water content and vegetation factors （height, cover, and biomass） were key environmental factors affecting soil ecological stoichiometry. The research results can provide scientific basis and practical guidance for the restoration of heavily degraded grassland in alpine meadows.

The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network （BPNN） and convolutional neural network （CNN） were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality； however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization （PSO） could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error （RMSE）, coefficient of determination （R²）, and mean absolute error （MAE） were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L^-1 to 0.210 9 mg·L^-1, reduced the MAE of the test set from 0.222 3 mg·L^-1 to 0.153 7 mg·L^-1 and increased the R² of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and R² of the test set in the CNN model were 0.122 0 mg·L^-1, 0.092 7 mg·L^-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.