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Abstract: In China, visibility condition has become an important issue that concerns both society and the scientific community. In order to study visibility condition trends and characteristics for the BTH (Beijing, Tianjin, and Hebei) area, meteorological data (1980–2008) were collected from over 100 ground stations. The 29-year average visibilities were 21.7, 13.6, and 18.0km for Beijing, Tianjin, and Hebei, respectively, and the long-term visibility of BTH exhibited decreasing trend before 1998, slight fluctuations between 1998 and 2006, and increasing trend between 2006 and 2008. Most of the visibility trend lines depicted the same fluctuations and converged to a very narrow range for the urban stations in each area. Average visibility at non-urban stations was apparently higher than that at urban stations, with 29-year average of 19.4 and 14.4km respectively. Visibility was higher in the spring and autumn for Tianjin and Hebei, but best in the winter for Beijing probably due to higher wind speed and lower relative humidity. The visibility spatial distribution showed lower visibility that primarily occurred in the urban areas of Beijing, Tianjin, and Shijiazhuang. The trends regarding days with haze, good visibility, and fog for the urban and non-urban areas of the BTH region were also discussed. There was an increasing trend for days with haze but no apparent trend for days with good visibility. On average, there were no more than nine foggy days per year in the BTH area. Visibility and PM2.5 were observed to negatively correlate at two stations in Beijing, where PM2.5 data are available, and aerosols could cause a greater degree of visibility impairment in the summer months according to the correlations between visibility and PM2.5 concentration. [Copyright &y& Elsevier]

Urban agglomerations are considered as significant space typologies in the post globalization & digitalization era. The spatial linkage intensity of cities in urban agglomeration is an important basis for evaluating the development and compactness of urban agglomerations. The requirements associated with the major national strategy to achieve the coordinated development of the Beijing-Tianjin-Hebei (BTH) region and the research methods made possible by Big Data in the Internet era have created the realistic possibility of revealing the strength of spatial networks and the spatial differentiation rules of urban agglomerations. This study uses Web Crawler to obtain 500,000 sets of Weibo data in 13 cities of the BTH urban agglomeration. Three criteria and nine indicators are used to construct an index system and a model to quantitatively evaluate the strength of spatial networks in the BTH urban agglomeration. The results show that spatial network connections between cities in the urban agglomeration are not strong overall, reflecting the limited development of urban agglomeration; the spatial networks in the urban agglomeration have hierarchical and centralized characteristics, which reflect the imbalanced development of the city network; Beijing, Tianjin, and Shijiazhuang are the centers of the social network connections in the BTH urban agglomeration, which means that Weibo's online space reinforces the existing urban system; there is a positive correlation between network spatial connectivity and the hierarchy of cities in the urban agglomeration, where cities with higher levels would have more urban network connections; Generally, Weibo network connections are stronger than economic or transport links between the cities, and the development of information technology may reduce the disparity of regional development. This research innovatively uses social network big data to reveal the strength of spatial network connections and spatial differentiation rules in the urban agglomeration. The methodology provided in this paper is systematic enough for generalization. • The spatial linkage intensity is an important basis for evaluating the development and compactness of urban agglomerations. • We construct a new index system using network big data to measure the strength of connection between cities. • The methodology can be applied to other regions when examining social network big data from Facebook or Twitter. [ABSTRACT FROM AUTHOR]