Urban heat island (UHI) effect decribes significant change due to rapid urbanization development. This study focused on the long time series analysis of UHI during the period 2000-2018, and analyzed the impact of land cover type and landscape metric factors on surface temperature. The results revealed that the UHI had a continuously decreasing trend in 2005–2010, and an increasing trend in 2000–2005 and 2010–2018. Cropland, built-up land, patch density (PD), Shannon Diversity Index (SHDI), and Landscape Shape Index (LSI) had a positive relationship with UHI, whereas forestland, open water, and CONTAG had a negative correlation with the UHI effect. The Geodetector analysis further revealed that PD, SHDI, and LSI had the greatest influences on LST as the three factors had the largest q values (0.287, 0.286, and 0.278). Forestland, cropland, and built-up land had greater impacts on the UHI than other land cover type factors. The explanatory power reached a maximum value of 0.408 when built-up land and cropland variables interacted. The findings of this study provide new understandings of the relationship between urban landscape and UHI, as well as important insights for urban planners to mitigate the UHI effect for the sustainable development of future urban agglomeration.

Three poly(ethylene glycol)–poly(ε-carprolactone) (PEG–PCL) copolymers with different topologies but identical molar ratio between PEG to PCL were designed. These copolymers, namely, diblock (L-PEG–PCL), triblock (B-PEG–PCL), and star shaped (S-PEG–PCL) copolymers, were extensively characterized by ¹H Nuclear Magnetic Resonance (¹H NMR), Fourier Transform Infrared Spectroscopy (FTIR), and Gel Permeation Chromatography (GPC) analyses. The effect of topology on crystallization was investigated by X-Ray Diffraction (XRD), Differential Scanning Calorimetry (DSC). Results showed that the diblock copolymer possessed the highest crystallinity, followed by triblock and star shaped copolymers. Although the topology did not affect the self-assembly behavior of copolymers, the effects on size, size distribution, drug loading content, and drug release rate of the polymeric micelles were observed. The micelles self-assembled from linear diblock copolymer achieved higher cellular uptake and lower half maximal inhibitory concentration (IC₅₀). These findings are favorable to establish the foundation to further design proper structure of amphiphilic copolymers as nanocarrier systems for efficient anticancer therapy.

Ambient fine particulate matter (PM_2.5) can cause respiratory and heart diseases, which have a great negative impact on human health. While, as a fast-developing region, the Belt and Road (B&R) has suffered serious air pollution, more detailed information has not been revealed. This study aims to investigate the evolutionary relationships between PM_2.5 air pollution and its population-weighted exposure level (PWEL) over the B&R based on satellite-derived PM_2.5 concentration and to identify the key regions for exposure control in the future. For this, the study focused on the B&R region, covering 51 countries, ranging from developed to least developed levels, extensively evaluated the different development levels of PM_2.5 concentrations during 2000–2020 by spatial-temporal trend analysis and bivariate spatial correlation, then identified the key regions with high risk under different levels of Air Quality Guidelines (AQG). Results show that the overall PM_2.5 and PWEL of PM_2.5 concentration remained stable. Developing countries presented with the heaviest PM_2.5 pollution and highest value of PWEL of PM_2.5 concentration, while least developed countries presented with the fastest increase of both PM_2.5 and PWEL of PM_2.5 concentration. Areas with a high level and rapid increase PWEL of PM_2.5 concentration were mainly located in the developing countries of India, Bangladesh, Nepal, and Pakistan, the developed country of Saudi Arabia, and least developed countries of Yemen and Myanmar. The key regions at high risk were mainly on the Indian Peninsula, Arabian Peninsula, coastal area of the Persian Gulf, northwestern China, and North China Plain. The findings of this research would be beneficial to identify the spatial distributions of PM_2.5 concentration exposure and offer suggestions for formulating policies for the prevention and control PM_2.5 air pollution at regional scale by the governments.

Fine particulate matter (PM_2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM_2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM_2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM_2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM_2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM_2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM_2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM_2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Atopic dermatitis (AD), chronic hand eczema (CHE), and urticaria are common inflammatory skin diseases among adolescents and associated with air quality. However, the synergistic effects of ambient air pollution and second-hand smoke (SHS) have been unclear. We conducted a cross-sectional study including 20,138 Chinese college students where dermatological examinations and a questionnaire survey were carried out. A generalized linear mixed model was applied for the association between individualized exposure of O₃, CO, NO₂, SO₂, PM_2.5, and PM₁₀ and the prevalence of inflammatory skin diseases. Interactions between air pollutants and SHS were analyzed. As a result, CO, NO₂, SO₂, PM_2.5, and PM₁₀ were positively correlated with the prevalence of AD, CHE, and urticaria. Higher frequency of SHS exposure contributed to increased probabilities of AD (p = 0.042), CHE (p < 0.001), and urticaria (p = 0.002). Of note, CO (OR: 2.57 (1.16–5.69) in third quartile) and NO₂ (OR: 2.38 (1.07–5.27) in third quartile) had positive interactions with SHS for AD, and PM_2.5 synergized with SHS for CHE (OR: 2.25 (1.22–4.15) for second quartile). Subgroup analyses agreed with the synergistic results. In conclusion, SHS and ambient air pollution are both associated with inflammatory skin diseases, and they have a synergistic effect on the prevalence of AD and CHE.