Industrial transformation has been regarded as an important measure to promote traditional village revitalization. Research on the spatial pattern and influence mechanism of industrial transformation in traditional villages is urgently needed. In this context, this study takes the 211 national traditional villages in Fujian Province of China as research objects and uses GIS spatial analysis and geographical detectors to analyze the spatial pattern and influence mechanism of industrial transformation in traditional villages. The results show that: (1) the scale of traditional village industry presents the characteristics of wavy growth. High- and medium-density cluster areas were identified. (2) Traditional villages can be categorized into three types, not transformed, to be transformed and transformed villages. These three stages of transformation have different features of industry development and different dominant industries. (3) The core factors affecting the industrial transformation of traditional villages show obvious differences at different transformation stages and spatial differences in coastal and inland areas. Therefore, policies and measures should be customized to local conditions to improve the development quality of traditional villages and promote the industrial transformation and upgrading of traditional villages. This study improves the research on the transformation and development mechanism of traditional village industry from the perspective of industrial revitalization in theory and provides experience and models as reference for the revitalization of traditional village industry in practice.

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM_2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM_2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM_2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM_2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM_2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM_2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.

As a heritage tourism and patriotism education in China, red tourism has strong value-expressive attributes, which makes it of great significance to examine self-congruity (SC) theory in a red destination. This study explores and compares the effects of SC and functional congruity (FC) on tourist satisfaction (TS) and destination loyalty (DL) by using structural equation modeling based on a sample of 332 tourists in Shaoshan city, a popular red destination in China. The findings indicate that SC of red tourists explains much higher variation of FC than that of other tourists. In addition, SC exerts equal effects on TS as FC and shows greater influence on DL than FC. This study sheds new lights on understanding the role of SC in destination marketing research and practices.

Long-term exposure to ozone pollution will cause severe threats to residents’ physical and mental health. Ground-level ozone is the most severe air pollutant in China’s Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial–temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD’s 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial–temporal kriging (STK) interpolation, and the STK model’s simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone’s spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.

An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area’s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast–southwest and the southeast–northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban–rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.

Simplifying and popularizing the preservation values (valuation methods) of national parks—based on the premise of accuracy—shows stakeholders the importance of national parks, and is the basis for exploring sustainable use and development mechanisms. However, there are hypotheses biases, strategic biases, and starting point biases in regards to the existing evaluation methods. Therefore, based on the results of the contingent valuation method of research, under bounded rationality, this study uses the two-stage dichotomous choice contingent valuation and selects three methods to estimate the willingness to pay (WTP) for preservation at Wuyishan National Park. The results support that the two-stage contingent valuation method could effectively evade uncertainty with a “willingness to pay” decision making under bounded rationality, and factually reflect the real WTP. The results show that: (1) the average willingness to pay (truncated) of each household in Wuyishan National Park is CNY 609 (USD 93.90), which is similar to the actual average tourism expenditure of each household. (2) The cultural worldviews and perceived restorative environment have significant impacts on willingness to pay. (3) Comparing the preservation value of Wuyishan National Park with the actual financial input plays a positive role in manifesting the importance of Wuyishan National Park and attracting more financial input. The preservation value of Wuyishan National Park in the key market is about six times that of the basic market and one-third of that in the national market, which provides a theoretical basis for selecting the key tourism development market of Wuyishan National Park. (4) Those respondents believe that more funds should be put into protecting the national parks for their sustainable existence and bequeathing to future generations, which shows that the construction of the national park system is significant in improving natural values. This study attempted to provide theoretical support for improving non-market value and sustainable development of national parks.