Urban air pollution is one of the most visible environmental problems to have accompanied China's rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran's I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variations of China's urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP) and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

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Bacterial type IV secretion system (T4SS) belongs to a growing class of evolutionarily conserved transporters that translocate DNA and proteins into a wide variety of organisms including bacterial and eukaryotic cells. Archetypal is the Agrobacterium tumefaciens VirB/D4 T4SS that transfers oncogenic T-DNA to various eukaryotic cells, which is transferred as a nucleoprotein T-complex with VirD2 as the pilot protein. As a derivative of plasmid conjugation systems, the VirB/D4 T4SS can also transfer certain mobilizable plasmids and bacterial proteins like VirE2 and VirF, although it is unknown how the membrane-bound T4SS recruits different transfer substrates. Here, we show that a cytoplasmic VirD2-binding protein (VBP) is involved in the recruitment of the T-complex to the energizing components of the T4SS, including VirD4, VirB4, and VirB11. VBP is also important for the recruitment of a conjugative plasmid to a different transfer system independent of VirB/D4. These data indicate that VBP functions as a previously unrecognized recruiting protein that helps couple nucleoprotein substrates to the appropriate transport sites for conjugative DNA transfers. VBP has three functionally redundant homologs, and similar homologs can be found in different bacterial genomes, suggesting a previously uncharacterized class of proteins involved in conjugative DNA transfers.bacterial type IV secretion system ͉ T-DNA ͉ virulence

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High concentrations of PM 2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM 2.5 concentrations for regional air quality control and management. In this study, PM 2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM 2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM 2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m 3. (2) PM 2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM 2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM 2.5. (3) The center of gravity of PM 2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM 2.5 concentrations have moved eastward, while low-value PM 2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The "High-High" PM 2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The "Low-Low" PM 2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM 2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM 2.5 concentration in China.

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The future development of new-type urbanization has drawn great attention from both the government and public alike. In this context, the present study had three related research aims. Firstly, it sought to predict the urbanization and population dynamics in China at both national and provincial levels for the period of 2015 to 2030. Secondly, on this basis, it sought to examine the spatial variation of urbanization given the predicted national urbanization rate of 70.12%. Thirdly, it sought to estimate and evaluate the national and provincial demands of investment in the development of new-type urbanization. The main conclusions from this study were as follows: (1) The population size and urbanization rate will reach 1.445 billion and 70.12%, respectively, from 2015 to 2030. (2) The demographic dividend will vanish when the population pressure reaches its maximum. During this period, there will be 70.16 million urban population born. The suburban population that becomes urbanized will be 316.7 million, and thus the net increase in urban population will reach 386 million. (3) Although the urbanization rate of every Chinese province will increase during 2015-2030, it will do so unequally, while differences in urbanization quality among provinces will also be substantial. In some provinces, moreover, the urbanization quality is not compatible with their eco-social development. (4) A total of 4,105,380 billion yuan is required to fund new-type urbanization and the investment demand for each province varies greatly; for example, Guangdong province requires the most funding, amounting to approximately 148 times that required by Tibet, the province in least need of funding. In the final part of this study, policy suggestions concerning the investment of the new-type urbanization are put forward and discussed.