No abstract

No abstract

No abstract

Identifying and separating the signal of urbanization effect in current temperature data series are essential for accurately detecting, attributing and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951-2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between the all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the ETI series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades.

Injectable self-healing hydrogels containing functional nanoparticles (NPs) have attracted much attention in many fields of biomedicine. A series of injectable self-healing hydrogels containing PEGylation CuS NPs based on N-carboxyethyl chitosan (CEC) and oxidized sodium alginate (OA) were developed by taking advantages of the unique functions of CuS NPs and chitosan, referred to as CuS NP hydrogels or CEC–OA m –CuS n , where “m” stands for the concentration percentage of the added OA solution (w/v) and “n” represents the molar concentration of CuS NPs in the hydrogels. The physical properties of CuS NP hydrogels, syringeability, rapid self-repair ability, and photothermal performance were systematically investigated. The multiple functions for CuS NP hydrogels requested in the skin healing process were explored. The results showed that CuS NP hydrogels had not only adjustable physical properties and good injectable self-healing characteristics but also excellent functionalities, concurrently including hemostatic ability, bacteria killing capability, and cell migration and proliferation promotion. In vivo wound healing and histomorphological examinations of immunofluorescence staining in a mouse full-thickness wound model demonstrated good acceleration effects of these hydrogels for infected wound healing. Therefore, these injectable self-healing CuS NP hydrogels which possess the abilities of hemostasis, antibacterial activity, and infected-wound healing promotion exhibit great potential as in situ wound dressings.

The valence band offset (VBO) of the wurtzite InN∕ZnO heterojunction is directly determined by x-ray photoelectron spectroscopy to be 0.82±0.23eV. The conduction band offset is deduced from the known VBO value to be 1.85∓0.23eV, which indicates a type-I band alignment for InN∕ZnO heterojunction.

Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (Tmean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean Tmean and minimum SAT (Tmin) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.

This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.