Antimicrobial polymers have emerged as a potential solution to the growing problem of antimicrobial resistance. Although several studies have examined the effects of various parameters on the antimicrobial and hemolytic activity of statistical copolymers, there are still numerous parameters to be explored. Therefore, in this study, we developed a library of 36 statistical amphiphilic ternary copolymers prepared via photoinduced electron transfer-reversible addition−fragmentation chain transfer polymerization to systematically evaluate the influence of hydrophobic groups [number of carbons (5, 7, and 9)] and chain type of the hydrophobic monomer (cyclic, aromatic, linear, or branched), monomer ratio, and degree of polymerization (DP n ) on antimicrobial and hemolytic activity. To guide our synthetic strategy, we developed a pre-experimental screening approach using C log P values of oligomer models, which correspond to the logarithm of the partition coefficient of compounds between n-octanol and water. This method enabled correlation of polymer hydrophobicity with antimicrobial and hemolytic activity. In addition, this study revealed that minimizing hydrophobicity and hydrophobic content were key factors in controlling hemolysis, whereas optimizing antimicrobial activity was more complex. High antimicrobial activity required hydrophobicity (i.e., C log P, hydrophobicity index) that was neither too high nor too low, an appropriate cationic/hydrophobic balance, and structural compatibility between the chosen monomers. Furthermore, these findings could guide the design of future antimicrobial ternary copolymers and suggest that C log P values between 0 and 2 have the best balance of high antimicrobial activity and low hemolytic activity.

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The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO2 concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO2 and various factors affecting the spatial distribution of CO2, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO2 column concentration (XCO2) with full spatial coverage in the Beijing–Tianjin–Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R2) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO2 concentrations from 2015 to 2019 in the Beijing–Tianjin–Hebei region was conducted using the established model. The study of the spatial distribution of XCO2 concentration in the Beijing–Tianjin–Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO2 map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Quantification of the relationship between agricultural water use and social development is important for the balance between conserving water resources and sustainable economic development. The agricultural water footprint (AWF) from crop production across 11 provinces in the Yangtze River Basin (YRB) of China, from 1999 to 2018, was calculated in the current paper. The driving factors which affected the provincial AWF were revealed using the logarithmic mean Divisia index (LMDI) model, based on a temporal and spatial variation assessment. The results showed that, with a growth rate of 1.95% per year, the annual AWF of the in the basin was 441.6 Gm3 (green water accounted for 73.63% of this) in the observed two decades. The Jiangsu, Anhui, Hubei and Sichuan provinces jointly accounted for 54% of the total AWF of the region. Cereal, cotton and fruit crops contributed most of the AWF, and determined the trends of the AWF over time. With the development of the economy and market demand, the dominant crop contributing to the AWF has shifted, from cereal and cotton around 2000, to cereals and fruits at present. The economic level was the main contributing factor driving the AWF. However, water use intensity was the most important factor which inhibited the growth of the AWF. Irrigation technology and the degree of urbanization also played a certain inhibitory role. There were significant differences in the driving effects among the different provinces. A comprehensive evaluation of the AWF and analysis of its driving factors provides a solid foundation for optimizing planting structure, strengthening water resource management, and enhancing regional exchanges and cooperation.