Effective treatment of pesticide residue-induced pollution in the aqueous environment is the key to improving the water quality of rivers and lakes. Modified biomass material (Fe-Al-PS) was successfully prepared by impregnating Fe and Al bimetallic compounds to peanut shell powder for adsorption of glyphosate isopropylamine salt herbicide residues in aqueous environments. Fe-Al-PS reached adsorption equilibrium for 10 mg·L-1 of glyphosate isopropylamine salt at the adsorbent dosage and adsorption time of 0.14 g and 10 min, respectively, and the removal rates were stabilized at 99.9 % and 99.6 %, respectively. The adsorption process followed the pseudo-secondary kinetic and Freundlich adsorption isotherm models and belonged to multi-molecular layer chemisorption. The removal of glyphosate isopropylamine salt by Fe-Al-PS was greater than 95 % in a wide range of pH (2-11). The thermodynamic results indicated that the adsorption was a spontaneous exothermic process. Fe-Al-PS materials were easy to access and involved simple synthesis and low energy consumption, had high anti-interference ability, were reusable, and could be used not only for the effective removal of glyphosate isopropylamine salt herbicide in real water bodies but also for the removal of inorganic phosphorus.
Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to
Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.
The impact of microplastics (MPs) as a new type of pollutant on water pollution has become a research hotspot. To explore the response relationship between the abundance of MPs and nitrogen metabolism function in a freshwater environment, Lake Ulansuhai was used as the research object; the abundance of MPs in the water was detected using a Zeiss microscope, and the distribution characteristics of nitrogen metabolism functional bacteria and functional genes in the water were analyzed using metagenomics sequencing. The correlation analysis method was used to explore the relationship between the abundance of MPs and nitrogen metabolism functional microorganisms and nitrogen metabolism functional genes. The results showed that the presence of MPs in freshwater environments had a higher impact on Cyanobacteria and Firmicutes as the dominant phyla, and the presence of MPs promoted their enrichment and growth. Among the dominant bacterial genera, MPs promoted the growth of Mycobacterium and inhibited Candidatus_Planktopila more significantly, further indicating that in freshwater environments, MPs affected normal nitrogen metabolism by affecting microbial communities, and pathways such as carbon and nitrogen fixation and denitrification were important pathways for MPs to affect nitrogen metabolism. From the perspective of nitrogen metabolism functional genes, it was found that the abundance of MPs significantly affected some functional genes during nitrification (pmoA-amoA, pmoB-amoB, and pmoC-amoC), denitrification (nirK and napA), and dissimilatory nitrate reduction (nrfA) processes (P < 0.05). Moreover, the influence of MPs abundance on different functional genes in the same pathway of nitrogen metabolism varied, making the impact of MPs on aquatic environments very complex; thus, its harm to the water environment cannot be underestimated.
It is of great significance for the conservation of biodiversity in farmland ecosystems to study the diversity, structure, functions, and biogeographical distribution of soil microbes in farmland and their influencing factors. High-throughput sequencing technology was used to analyze the distribution characteristics of soil bacterial diversity, community structure, and metabolic function along elevation and their responses to soil physicochemical properties in farmland in the loess hilly areas of Ningxia. The results showed that:① The Alpha diversity index of soil bacterial was significantly negatively correlated with elevation (P < 0.05) and showed a trend of decreasing and then slightly increasing along the elevation. ② Seven phyla, including Proteobacteria, Actinobacteria, and Acidobacteria, were the dominant groups, and five of them showed highly significant differences between altitudes (P < 0.01). ③ At the secondary classification level, there were 36 metabolic functions of bacteria, including membrane transport, carbohydrate metabolism, and amino acid metabolism, of which 22 showed significant differences, and 12 showed extremely significant differences among different altitudes. ④ Pearson correlation analysis showed that soil water content, bulk density, pH, and carbon-nitrogen ratio had the most significant effects on bacterial Alpha diversity, whereas soil nutrients such as total organic carbon, total nitrogen, and total phosphorus had significant effects on bacterial Beta diversity. ⑤ Mantel test analysis showed that the soil water content, total organic carbon, and carbon-nitrogen ratio affected bacterial community structure at the phylum level, and soil pH, total organic carbon, total nitrogen, total phosphorus, and carbon-nitrogen ratio were significantly correlated with bacterial metabolic function. Variance partitioning analysis showed that soil water content had the highest explanation for the community structure of soil bacteria, whereas soil pH had the highest explanation for metabolic function. In conclusion, soil water content and pH were the main factors affecting the diversity, community composition, and metabolic function of soil bacteria in farmland in the loess hilly region of Ningxia.
Heavy metal pollution in farmland soil can affect the growth, development, and yield of vegetable crops, as well as the quality and taste of vegetables, and can be continuously transmitted and enriched through the food chain, which ultimately poses a certain hazard to human health in the long term. Therefore, in order to investigate the distribution characteristics of soil heavy metals after years of multi-crop planting of vegetables supplied to Hong Kong, predict their ecological risks, and analyze the causes of pollution formation, 477 surface soil samples of vegetable fields supplied to Hong Kong in Ningxia were collected for three consecutive years from 2019 to 2021, and the contents and distribution characteristics of eight heavy metals, namely, As, Cd, Cr, Hg, Pb, Cu, Zn, and Ni were analyzed. The soil heavy metal pollution status of vegetable fields supplied to Hong Kong in Ningxia was evaluated using the single-factor pollution index method, Nemero's comprehensive pollution index method, land accumulation index method, and potential ecological risk index method, and the sources of heavy metals in vegetable fields supplied to Hong Kong in Ningxia were analyzed using the Pearson's correlation analysis and the principal component analysis method. The results showed that the mean values of As, Cd, Cr, Hg, Cu, and Zn in the soils of Ningxia's vegetable fields were higher than the background values of Ningxia soils, but the contents of all eight heavy metals were lower than the risk screening values of domestic agricultural soils; in terms of spatial distribution, As, Cr, and Ni showed contiguous high values in the northwestern, central, and southern parts of the study area, whereas Pb, Zn, Cd, Hg, and Cu showed high values in the northwestern and southern parts of the study area. The single-factor index method and the Nemero's comprehensive pollution index method showed that the soil of Ningxia's vegetable farmland for Hong Kong was at the clean level as a whole. The results of the ground accumulation index method showed that the pollution in the study area was mainly Hg and Cd pollution, and the pollution areas were mainly concentrated in the northwest and south of the study area. The potential ecological risk index showed that Hg and Cd were the main risk elements, among which Hg was dominated by moderate, strong, and very strong ecological risks, accounting for 44.65 %, 44.65 %, and 1.26 %, respectively, and Cd was dominated by moderate and strong risks, accounting for 65.83 % and 3.56 %. The comprehensive Pearson correlation analysis and principal component analysis showed that the pollution sources of eight heavy metals could be divided into three categories, namely, natural sources:Cu, Zn, Pb, As, Ni, and Cr; agricultural sources:Cd; and industrial and agricultural sources:Hg. From a comprehensive point of view, the heavy metals of the soil in the fields of vegetables supplied to Hong Kong had not exceeded the standard, and the env
It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.
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