Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space-time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM2.5 removed natural dust (PM2.5_No Dust) concentrations and the corresponding population's PM2.5_No Dust exposure (PPM2.5E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM2.5_No Dust-polluted regions, whose spatial relative magnitude of PM2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM2.5_NoDust concentrations and PPM2.5E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM2.5_NoDust concentrations and PPM2.5E were greater in the medium stage than in the early and later stages. The local trends of PM2.5_NoDust concentration and PPM2.5E in the two higher PM2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM2.5_NoDust concentration and PPM2.5E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM2.5E to Europe. The PPM2.5E trend in Africa generally increased during the study period.

This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance and the land surface emissivity (LSE), the essential SWA input parameters, of the Landsat-8 TIRS data are determined in this paper. We also analysed the error sensitivity of these SWA input parameters. The accuracy evaluation of the proposed SWA in this paper was conducted using the software MODTRAN 4.0. The root mean square error (RMSE) of the simulated LST using the mid-latitude summer atmospheric profile is 0.51 K, improving on the result of 0.93 K from Rozenstein (2014). Among the 90 simulated data points, the maximum absolute error is 0.99 °C, and the minimum absolute error is 0.02 °C. Under the Tropical model and 1976 US standard atmospheric conditions, the RMSE of the LST errors are 0.70 K and 0.63 K, respectively. The accuracy results indicate that the SWA provides an LST retrieval method that features not only high accuracy but also a certain universality. Additionally, the SWA was applied to retrieve the LST of an urban area using two Landsat-8 images. The SWA presented in this paper should promote the application of Landsat-8 data in the study of environmental evolution.

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors—population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)—on remotely sensed ${\mathrm{PM}}_{2.5}$ concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of ${\mathrm{PM}}_{2.5}$ annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on ${\mathrm{PM}}_{2.5}$ annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of ${\mathrm{PM}}_{2.5}$ annual concentration over China with a high spatial influencing magnitude of 96.65%.

A soundscape carries the culture and memories of historical and cultural areas, capable of evoking people’s emotions towards a place. This paper aims to explore the influence of the soundscape on the sense of place in historical and cultural areas using a mixed-method approach. Conducting on-site measurements, questionnaires, and semi-structured interviews in four areas in Taiyuan, China, this research utilized structural equation modeling and grounded theory for analysis. Research findings: Firstly, the soundwalk method identified 11 categories of soundscapes and the main sources of noise in residential areas were traffic sounds and commercial sounds. Secondly, residents showed the highest preference for natural sounds and traditional activity sounds. Preferences for activities’ sounds and traditional sounds, along with the perceived frequency of daily life sounds, entertainment activities’ sounds, and natural sounds were positively associated with the sense of place, while the perceived frequency of commercial sounds, traffic sounds, and pet sounds was negatively associated. Lastly, from the grounded theory, this research showed that traditional sounds effectively enhanced residents’ sense of place by triggering memories, embodying culture, and connecting emotions. Daily life sounds linked residents’ lives and prompted visions, while local dialects evoked emotion and culture. Activities’ sounds served as lubricants for the place, primarily triggering residents’ sense of place from a lifestyle and emotional connection.

With the rapid industrial development and urbanization in China over the past three decades, PM_2.5 pollution has become a severe environmental problem that threatens public health. Due to its unbalanced development and intrinsic topography features, the distribution of PM_2.5 concentrations over China is spatially heterogeneous. In this study, we explore the spatiotemporal variations of PM_2.5 pollution in China and four great urban areas from 1998 to 2014. A space-time Bayesian hierarchy model is employed to analyse PM_2.5 pollution. The results show that a stable “3-Clusters” spatial PM_2.5 pollution pattern has formed. The mean and 90% quantile of the PM_2.5 concentrations in China have increased significantly, with annual increases of 0.279 μg/m³ (95% CI: 0.083−0.475) and 0.735 μg/m³ (95% CI: 0.261−1.210), respectively. The area with a PM_2.5 pollution level of more than 70 μg/m³ has increased significantly, with an annual increase of 0.26 percentage points. Two regions in particular, the North China Plain and Sichuan Basin, are experiencing the largest amounts of PM_2.5 pollution. The polluted areas, with a high local magnitude of more than 1.0 relative to the overall PM_2.5 concentration, affect an area with a human population of 949 million, which corresponded to 69.3% of the total population in 2010. North and south differentiation occurs in the urban areas of the Jingjinji and Yangtze Delta, and circular and radial gradient differentiation occur in the urban areas of the Cheng-Yu and Pearl Deltas. The spatial heterogeneity of the urban Jingjinji group is the strongest. Eighteen cities located in the Yangtze Delta urban group, including Shanghai and Nanjing, have experienced high PM_2.5 concentrations and faster local trends of increasing PM_2.5. The percentage of exposure to PM_2.5 concentrations greater than 70 μg/m³ and 100 μg/m³ is increasing significantly.