The world has experienced dramatic urbanization in recent decades. However, we still lack information about the characteristics of urbanization in large cities throughout the world. After analyzing 841 large cities with built-up areas (BUAs) of over 100 km2 from 2001 to 2018, here we found an uneven distribution of urbanization at different economic levels. On average, large cities in the low-income and lower-middle-income countries had the highest urban population growth, and BUA expansion in the upper-middle-income countries was more than three times that of the high-income countries. Globally, more than 10% of BUAs in 325 large cities showed significant greening (P < 0.05) from 2001 to 2018. In particular, China accounted for 32% of greening BUAs in the 841 large cities, where about 108 million people lived. Our quantitative results provide information for future urban sustainable development, especially for rational urbanization of the developing world.

Surface modified ZnO quantum dots (QDs) with ultrastable, strong and tunable luminescence have been successfully prepared via silanization during the growth process by (3-(2,3-epoxypropoxy)propyl)trimethoxysilane. The as-prepared ZnO QDs are demonstrated to be promising for anti-counterfeit applications in expensive high-end liquors, etc.

Pathogenic bacterial contamination is at the root of many persistent and chronic bacterial infections. Synergistic superhydrophobic surfaces functionalized with a cationic antimicrobial agent (e.g., quaternary ammonium components) show promising antimicrobial efficacies, but are inherently contradictory in simultaneously maintaining excellent surface liquid repellency and bactericidal activity due to the compromised low surface energy stemming from the introduced hydrophilic biocides. Herein, we present a synergistic antibacterial cotton textile with superhydrophobic bacterial repellency and photodynamic bactericidal activity for both Staphylococcus aureus and Escherichia coli. The modified cotton textile was constructed by integrating tunable micro/nanoscale roughness, hydrophobic photosensitizer chlorin e6, and surface perfluorination. The triple-scale structured superhydrophobic surfaces exhibited ≲90% reductions in both waterborne and airborne bacterial adhesions. Subsequently, after being exposed to visible light for 45 min, the synergistic surface demonstrated complete inactivation (100% killing) against residual bacterial cells via photodynamic bactericidal capacities. Moreover, the triple-scale structured superhydrophobic surface displayed totally suppressed whole blood adhesion, suggesting potential in preventing plenty of undesirable biomedical forms of contamination. This synergistically antibacterial surface not only improves the antibacterial efficiency but also leads to long-lasting antimicrobial performance, each of which is highly desirable in combating bacterial infections.

Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed-Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R 2) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community.

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