The value of a cultural ecosystem service depends on the perception of different cultural service categories. However, the data sources used in research on the perception of cultural service have limitations that mainly depend on social investigation, leading to slow progress in cultural service evaluation. With the advent of the era of network big data, social media provides a new data source for the study of cultural ecosystem service perception, so that the study of these services is expected to make new breakthroughs. Using search crawler software, this paper reviewed 7257 online comments related to 19 city parks in Xuzhou City, China. With the help of Rost Content mining semantic analysis software, the comment sentences were divided into keywords, and the Delphi expert method was used to classify these keywords. Thus, a cultural service perception database was established. Through statistical analysis, with the help of ArcGIS software, various cultural services were analyzed. The results showed that (1) the cultural services of urban parks could be divided into seven types (i.e., aesthetics, recreation, sports, inspiration, education, cultural heritage, and spiritual satisfaction) using social network comment data. (2) High-frequency keywords of online comment data can serve as the core basis during an analysis of the perception of cultural services by visitors of city parks. However, a large gap exists in the number of high frequency keywords in different parks. For example, Yunlong Lake Park has 2887 keywords, while Kuaizai Ting Park has only 33. (3) Differences exist in the perception of cultural service in urban parks, the park’s scale, and characteristics determine the visitor’s cultural service perception level. The aesthetic and recreation types were the most easily perceived, and 68% and 63% parks have the above two perceptual records, respectively. Therefore, the social media comment data has the ability to document perception of each park’s cultural service type and its differences, which can serve as the cultural ecosystem service perception as well as the valuation data source, to supplement the social investigation.

Improvement of soil quality after land reclamation is a key concern in mining areas. However, the characteristics and internal mechanisms of variation of bacterial community structure over different reclamation periods are currently unclear. The recovery and evolution of soil microbial community structure are important indicators of the level of soil quality improvement of reclaimed soil. Therefore, this study investigated soil samples from coal gangue-filled land after reclamation periods of 1, 6, and 15 years. To accomplish this, 16S rRNA gene libraries were produced to determine the microbial community composition of the soils. In addition, various soil microbial community characteristics in the filled reclamation areas were compared with soil samples from areas unaffected by coal mining. The results showed the following: (1) The diversity and abundance of bacterial communities in reclaimed soils was slightly different from that of natural soils. However, the soil bacterial community structure was highly similar to natural soil after a 15-year reclamation period; therefore, the recovery of bacterial communities can be used as an indicator of the effects of rehabilitation.(2) Some soil physicochemical properties are significantly correlated with the main bacteria in the soil.(3) The dominant bacteria included members of the phyla Firmicutes and Proteobacteria, as well as members of the genera Bacillus, Enterococcus, and Lactococcus. Taken together, the results of this study indicated that the application of microbial remediation technology can be used to adjust the soil microbial community structure, improve soil quality, and shorten the soil recovery period.

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Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

CO2-EOR (enhanced oil recovery) has been proposed as a viable option for flooding oil and reducing anthropogenic CO2 contribution to the atmospheric pool. However, the potential risk of CO2 leakage from the process poses a threat to the ecological system. High-throughput sequencing was used to investigate the effects of CO2 emission on the composition and structure of soil bacterial communities. The diversity of bacterial communities notably decreased with increasing CO2 flux. The composition of bacterial communities varied along the CO2 flux, with increasing CO2 flux accompanied by increases in the relative abundance of Bacteroidetes and Firmicutes phyla, but decreases in the relative abundance of Acidobacteria and Chloroflexi phyla. Within the Firmicutes phylum, the genus Lactobacillus increased sharply when the CO2 flux was at its highest point. Alpha and beta diversity analysis revealed that differences in bacterial communities were best explained by CO2 flux. The redundancy analysis (RDA) revealed that differences in bacterial communities were best explained by soil pH values which related to CO2 flux. These results could be useful for evaluating the risk of potential CO2 leakages on the ecosystems associated with CO2-EOR processes.