工业工程最新文献

This study calculates the carbon emission performance of 277 Chinese cities from 2006 to 2020 with the help of an SBM-DEA model, displays its evolution characteristics using the ArcGIS platform, and empirically tests and analyzes the local and spatial spillover effects of economic transformation on carbon emission performance using the spatial Durbin model. The results showed that： ① The carbon emission performance level of Chinese cities was generally high, but the trend of decline was obvious, and the distribution showed the characteristics of low-level agglomeration from equilibrium distribution to U-shaped transformation. ② At the current stage, the local promoting effect of economic transformation on the improvement of carbon emission performance has not been fully revealed. The marketization, globalization, and fiscal decentralization of local urban economic transformation significantly improved the carbon emission performance of local cities, but the decentralization of administrative categories, the intensity of environmental regulation, and the level of green technology innovation inhibited the improvement of carbon emission performance. ③ Carbon emission performance was spatially dependent. The globalization level of economic transformation, environmental regulation intensity, green technology innovation level, and improvement of carbon emission performance of neighboring cities had a positive spillover effect on local carbon emission performance, while the decentralization level of administrative categories had a negative spillover effect. Clarifying the level and distribution of urban carbon emission performance and explaining it from the perspective of economic transformation is of great practical significance for promoting the realization of the "double carbon" goal in China and other countries.

To understand the niche and interspecific association of phytoplankton in different hydrological periods of the Xiaoche River in Guizhou Province, phytoplankton samples were collected in two different hydrological periods （dry period and wet period） from 2020 to 2022. The niche width （B_i），niche overlap（O_ik），variance ratio method（VR），chisquare test（χ²）, AC test, and Jaccard index （JI） were used to analyze the niche and interspecific association of phytoplankton dominant species. The results showed that a total of 7 phyla and 50 species of phytoplankton were identified in the Xiaoche River, with 4 phyla and 12 species being the dominant species. The combination analysis of niche width and FG functional group showed that the water environment of the Xiaoche River in a medium eutrophic state. The dominant species could be divided into broad niche species, medium niche species, and narrow niche species, and the niche width of the dominant species varied between 1.278 and 4.849. No significant difference exists in the three years of niche overlap value, with the proportion of O_ik>0.6 being 50.00% （2020）, 42.86% （2021）, and 53.49% （2022）, respectively, indicating that there were close to the same or more common species occupying environmental resources in a highly similar way. The dominant species showed a significant positive correlation overall, and the community was relatively stable. The chi-square test showed that only four pairs of 66 dominant species had significant associations； the AC test showed that 33 pairs had significant associations； and the Jaccard index showed that 41 pairs had significant associations, all of which showed more negative than positive associations, indicating that the phytoplankton community in the Xiaoche River was in the early stage of succession.

In the context of the deepening of the "dual carbon" strategy, based on the customs database and the energy consumption database, starting from the "city-industry" scale, this study characterizes and analyzes the spatiotemporal distribution characteristics of carbon emissions from double-digit industrial industries in prefecture-level cities in China. We constructed a fixed effects model using panel data and studied the impact and transmission mechanism of industrial correlation, empowering industrial carbon reduction. Moreover, it was revealed that： ① The carbon emission intensity of various industries in the eastern and central western regions had decreased, and the difference in industry carbon emissions between the two regions had been significantly reduced. The carbon emission intensity of the same industry category in the central and western regions decreased more significantly, and the effectiveness of industrial carbon reduction measures was significant. ② Improving the level of industry correlation within cities was an alternative path to reducing carbon emissions. For every 1% increase in industrial correlation, the average carbon emission intensity decreased by 0.234%, a result that still held after a series of robustness tests. ③ This effect was more significant in capital-intensive industries, middle- and low-end technology industries, western regions, and cities with stronger government intervention. ④ The quality of technological innovation and the vitality of the digital economy played an important intermediary role in the carbon emission reduction effect of industrial linkage. Industrial linkage promoted the reduction in industrial carbon emission intensity by improving the quality of technological innovation and economic innovation vitality. The research results uncovered the "black box" of the effectiveness of industry correlation in promoting industry carbon reduction at the "city-industry" scale, which can provide new decision-making references for achieving coordinated and unified regional industrial development and low-carbon transformation.

The effect of passivating agents on Cd and As is closely related to soil properties. Optimizing passivating agents that adapt to soil properties is the key basis for the application of passivation technology. This study uses eight types of purple soils with widely different properties as test soils, uses indoor culture experiments, sets different pollution conditions, and simultaneously compares the passivation rates of cadmium （Cd） and arsenic （As） by seven common passivators. Additionally, combined with the measurement of soil physical and chemical properties, the relationship between passivation efficiency and soil properties was explored. The results showed that among the seven common passivators tested, calcium oxide, organic fertilizer, silicon calcium magnesium fertilizer, humic acid, and hydroxyapatite had a significant passivation effect on purple soil Cd, and iron oxide, silicon calcium magnesium fertilizer, hydroxyapatite, and humic acid had a passivating effect on As. Only three passivating agents, calcium silicon magnesium fertilizer, hydroxyapatite, and humic acid, had a passivating effect on both Cd and As. Great differences exist in the key soil property factors that determined the passivation efficiency of specific passivators： soil organic matter, clay content, and total potassium were significantly positively correlated with the passivation rate of soil Cd by the tested passivators； soil pH, free iron oxide, free manganese oxide, and total phosphorus were significantly negatively correlated with them； soil CEC, free manganese oxide, and soil total As were significantly positively correlated with the As passivation rate of the tested passivators； and the content of soil clay particles was significantly negatively correlated with it. The study established the optimal multiple linear regression model between the passivation efficiency of passivating agents Cd and As and the soil properties and pollution characteristics of purple soil. The model quantitatively reflects the relationship between the passivation efficiency of heavy metals and soil properties and can be used to predict and optimize adaptation accordingly. Highly efficient passivators with different soil properties provide a scientific basis for the safe use of regionally contaminated farmland.

Soil aggregates, the fundamental units of soil structure, crucially regulate soil physicochemical properties. Acidification alters soil aggregation, impacting heavy metal distribution and availability within aggregates. This study explores aggregate composition in differently acidified yellow and purple soils, along with the variation in the distribution and availability of cadmium （Cd） and lead （Pb） in different-sized aggregates. Acidification reduced the mass fraction of large aggregates （>2 mm）, with non-acidified soil being 5%-15% higher. In both soils, large aggregates contributed most to the total amount of Cd and Pb （contribution factors 0.31-0.47）. Yellow soil showed the highest Cd and Pb contents in small （1-0.25 mm） and micro-aggregates （<0.25 mm）, while the highest contents were observed in large aggregates in acidified purple soil. The mass fractions determined the distribution of external Pb and Cd in aggregates when entered into soils. In highly acidified soil, smaller aggregates posed a higher heavy metal release risk, while in non-acidified soil, the large aggregates showed higher Cd and Pb contents and thus a higher release risk. The alterations in the transformation and availability of Cd and Pb were attributed to the variations in soil aggregate composition and their properties driven by acidification, including mineral weathering, iron oxide leaching, organic matter loss, etc. These results provide the basis for the co-remediation of soil acidification and heavy metal pollution.

Considering Henan Province as the research area, based on land use data with a resolution of 30 m in 2000, 2010, and 2020, we analyzed the distribution of the production-living-ecological space and the quality of the ecological environment in Henan Province using land transfer matrices and an eco-environmental effect model. Furthermore, based on 2020 land use data, the PLUS model was used to simulate land use data for the years 2030, 2040, and 2050 under three scenarios： natural development, production priority, and ecological priority. Finally, we calculated the ecological environment quality index and ecological contribution rate. The results showed that： ① From 2000 to 2020, the area of production space decreased by 4 879 km², the area of living space increased by 19%, and the area of ecological space increased by 1.9%. ② From 2000 to 2020, the eco-environmental quality index increased from 0.364 6 in 2000 to 0.366 5 in 2010 and then decreased to 0.365 7 in 2020, which was at a medium level. ③ From 2030 to 2050, the distribution of production-living-ecological space will remain unchanged, and the trend under the natural development and ecological priority scenarios will remain unchanged. ④ From 2020 to 2050, Henan Province had the best ecological environment under the ecological priority scenario, and the ecological environment quality index was predicted to be 0.365 7 in 2030, 0.366 0 in 2040, and 0.366 5 in 2050. The distribution of production-living-ecological space is of great importance for the future development of the earth's space and the construction of an ecological environment.

Based on the observational data of volatile organic compounds （VOCs）, conventional air pollutants, and ERA5 meteorological reanalysis data at three sites, namely, Caochangmen （CCM）, Pukou （PK）, and Xianlin University Town （XL）, in Nanjing from 2015 to 2021, the ozone generation and depletion mechanisms in ozone-polluted days under stable weather conditions were investigated using the observation-based model （OBM-MCM）. The results showed that ① Significant year-by-year differences exist in the frequency of stable weather on ozone-polluted days for the three sites. The maximum number of stable days occurred in 2019, with 46 d （66.7%）, 50 d （64.9%）, and 54 d （69.2%） at the CCM, PK, and XL sites, respectively. ② Significant differences exist between the net O₃ production rates for the CCM, PK, and XL sites during the polluted period, with the highest rate of 2.5×10^-9 h^-1 at the CCM site and the lowest rate of 1.4×10^-9 h^-1 at the XL site. Additionally, the O₃ production and depletion rate at the XL site were lower compared to those at the other two sites. ③ The reactions of HO₂·+NO and ·OH+NO₂, respectively, contributed the most to O₃ production and depletion. The HO₂·+NO reaction contributed to O₃ production by 69% （CCM）, 68% （PK）, and 71% （XL）, and the ·OH+NO₂ reaction contributed to O₃ depletion by 67% （CCM）, 63% （PK）, and 62% （XL）. ④ The modeling study observed that ozone pollution under stable weather conditions was mainly affected by local photochemistry processes； therefore, local emission reduction is very important for O₃ pollution mitigation.

Based on the surface meteorological data and ambient air quality data of Taiyuan from 2016 to 2020, the temporal and spatial variation characteristics of PM_2.5 concentration in Taiyuan were analyzed. The temporal and spatial variation characteristics of PM_2.5 concentration in Taiyuan were studied using the EOF decomposition diagnostic analysis method. At the same time, the importance of meteorological factors was analyzed using a random forest model, and a PM_2.5 concentration prediction model based on the CNN-LSTM neural network was established. The results showed that from 2016 to 2020, the annual mean PM_2.5 concentration in the urban area of Taiyuan generally exhibited a decreasing trend from year to year, and the high value mainly appeared in November, December, January, and February. From 18：00 to 02：00 of the next day, the peak value of PM_2.5 concentration was easily reached, and the annual average value of PM_2.5 concentration gradually increased from northwest to southeast. The EOF decomposition of PM_2.5 concentration was as follows： the variance contribution rate of modal 1 eigenvector was 49.4%, and the variance contribution rate of modal 2 eigenvector was 30.8%. Considering Nanzhai-Julun-Jinyuan as the boundary, it was a positive area to the northwest and a negative area to the southeast. The positive center appeared in Jinsheng district, and the negative center appeared in Xiaodian in the southeast. PM_2.5 concentration was positively correlated with relative humidity and dew point temperature. Moreover, it was mainly negatively correlated with wind speed, precipitation, and mixing layer height and generally negatively correlated with ventilation and self-purification capacity, with no significant correlations involving temperature. Relative humidity, dew point temperature, air pressure, humidity, and mixing layer height all played an important role in the ranking of the four seasonal characteristics, followed by wind speed, wind direction, ventilation volume, and self-purification capacity. Using the CNN-LSTM model for modeling, the R² of PM_2.5 concentration prediction was 0.805, 0.826, 0.897, and 0.901 in spring, summer, autumn, and winter, respectively. R² was above 0.8 in all four seasons. The predicted residuals of the CNN-LSTM model in all four seasons were approximately normally distributed, and the absolute error of the model was controlled within 10 μg·m^-3. The prediction results below 10 μg·m^-3 reached a maximum of 81.2% in summer, followed by 75.9% and 62.9% in autumn and spring, respectively. The performance in winter was average, with 51.5% of the prediction results having an absolute error below 10 μg·m^-3.

Soil organic carbon （SOC） stability and carbon sequestration potential can be affected by vegetation cover. The Caohai in Guizhou is a typical plateau freshwater wetland ecosystem at the same latitude as other regions worldwide. The study focused on the soils from Caohai and selected five types of vegetation cover, including forest land, cultivated land, grassland, reeds, and tidal flats. Soil samples were collected vertically at depths of 0-20, 20-40, and 40-60 cm, and the activity and stability of SOC were tested to explore the impact of different vegetation covers on the composition and stability of SOC, finally providing basic data for soil carbon sequestration and ecological protection in the Caohai. The results indicated that cover variability led to differences in SOC content on the vertical distribution scale （P<0.05）. Comparing SOC content in various covers with tidal flats （14.75 g·kg^-1） on a horizontal scale, reductions were observed in forests （19.32%）, reeds （15.05%）, cropland （12.47%）, and grassland （1.58%）. The proportions of stable and labile organic carbon （SAOC and LOC） accounted for 52.51% and 45.00% of the total SOC content, respectively. The ω（ROC） （g·kg^-1） values were 7.89 （forests）, 7.47 （tidal flats）, 6.67 （reeds）, 6.36 （cropland）, and 5.67 （grassland）, with N/P and TN being the main physicochemical factors influencing ROC （r≥0.72）. ROCI ranged from 0.16 to 0.34, with the highest in forest land and relatively lower values in cropland and grassland, mainly affected by N/P （P<0.01）, negatively correlated with BD （P<0.05）, and influenced by cover type （P<0.001） and soil depth （P<0.01）. Both cover type and soil depth interacted significantly with ROCI （P<0.01）. The research indicated that vegetation cover variability altered organic matter decomposition and accumulation, leading to a differential distribution of organic carbon components. Agricultural activities affected the stability and accumulation status of organic carbon, contributing to the higher levels of unstable carbon components SAOC and LOC and exhibiting significant potential for soil carbon sequestration enhancement. Moreover, it is recommended to implement scientific management practices to transition towards a more stable state, thereby enhancing regional ROCI levels and carbon sequestration potential.

Based on the observation and reanalysis data in Guangdong from 2015 to 2020, the variation characteristics of ozone （O₃） concentration in Guangdong and four typical cities were analyzed, and the effects of meteorological factors on O₃ concentration in different cities and different seasons were revealed based on the generalized additive model （GAM）. The daily maximum 8-hour average O₃ concentration （O₃_8h） increased significantly from 2015 to 2019, with a trend of 5.0 μg·m^-3·a^-1, and decreased slightly in 2020 in Guangdong. The O₃_8h concentration and the total number of polluted days were substantially higher in autumn than those in other seasons in Guangdong and showed different variation characteristics in four typical cities. The highest values in Guangzhou, Heyuan, Jieyang, and Maoming occurred in summer and autumn, spring and summer, spring and autumn, and autumn, respectively. The regression model had a good fit for the variation in O₃_8h concentration, and the seasonal models were generally better than the annual models. As for the seasonal models, the average R² values were 0.78, 0.69, 0.70, and 0.65, and the mean interpretation rate of variance （IRV） values were 79%, 71%, 73%, and 67% in Guangzhou, Heyuan, Jieyang, and Maoming, respectively. The equation fitting degrees of the optimal models varied considerably in different cities and different seasons, with the R² values ranging from 0.52 to 0.83 and the IRV values ranging from 55.5% to 86.9%. The O₃_8h concentration showed a nonlinear relationship with meteorological factors. The meteorological factors that had a significant impact on the variation of O₃_8h concentration in different cities and seasons differed considerably, and all of the meteorological factors were in the top three lists of importance. Relative humidity was the most important meteorological factor affecting the variation in O₃_8h concentration in different cities, followed by the V-component of wind. When the relative humidity was below 45%, the O₃_8h concentration was relatively higher. When the relative humidity was above 45%, the O₃_8h concentration decreased with the increase in relative humidity. Higher O₃ concentrations appeared when the wind speed was greater than 2 m·s^-1, indicating the regional transport of pollutants and emphasizing the importance of regional joint prevention and control.