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The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China. [ABSTRACT FROM AUTHOR]

Ecological resilience reflects the role of human activity intensity (HMI) on regional ecosystem services (ESs), and resilience improvement is crucial for the high-quality development of urban agglomeration areas. However, a theoretical framework for ecological resilience needs to be developed based on ES thresholds under human activities. Based on the threshold index, we used threshold regression model to determine of the nonlinear dominant factors affecting ESs and to identify the priority areas for ecological restoration. The results suggest that (1) CS, HQ, and TES declined, while FP increased. The spatial distribution of each ES showed higher values in the central region and lower values in the surrounding areas. HMI showed a significant upward trend, with expanding high-HMI areas. There is a threshold effect in the relationship between HMI and ESs, leading to variations in their positive, non-monotonic, or non-linear interactions. (2) HMI positively and significantly affects ESs, especially at low threshold levels. The effect of HMI on ESs is negative in counties with higher threshold levels rather than in lower-level grids. (3) The critical area of artificial potential ecological restoration was 712 km2, primarily concentrated around urban; the critical area of natural restoration was 490 km2. Therefore, the threshold in the relationship between HMI and ESs should be given more attention. This study serves as a guide for picking out key regions for territorial ecological restoration. [ABSTRACT FROM AUTHOR]

Colored dissolved organic matter (CDOM) is highly spatiotemporally varied due to the effects of complex environmental factors within a catchment or system. The seasonal nutritional status and potential risks of heavy metals in the coastal rivers of the Liaohe River basin were evaluated based on 40 water samples in January, April, May, and September. Meanwhile, the effects of environmental factors on CDOM, especially human activities, were quantitatively analyzed. The trophic state index (TSI) and the potential ecological risk index (RI) of heavy metals in the Liaohe River basin exhibited significant differences. The rivers were mesotrophic in January, lightly eutrophic in May, and highly eutrophic in April and September. An extremely high RI was shown in April and May, while a high RI was exhibited in September. CDOM exhibited great seasonal characteristics and showed significant seasonal correlations with environmental factors. Based on multiple general linear model analysis, total phosphorus (TP) was the most influential factor and significantly explained 62.1% of aCDOM(440) (p < 0.01) among the water parameters, followed by total alkalinity (38.3%). The percentages of built-up area exerted significantly positive effects on aCDOM(440) (R2 = 0.44), while distance from oil extraction sites significantly negatively affected aCDOM(440) (r = − 0.328, p < 0.05). Polluting enterprises showed non-significant correlation with CDOM (r = 0.314, p = 0.178). [ABSTRACT FROM AUTHOR]

Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects, including fully understanding the causes and mechanisms of climate change, actively adapting to climate change, pursuing rational development, and protecting the ecological environment. Taking the north slope of Tianshan Mountains, located in the arid area of northwestern China and extremely sensitive to climate change, as the research area, this study retrieves the surface temperature of the mountain based on MODIS data, while characterizing the intensity of human activities thereby data on the night light, population distribution and land use. The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed, and the spatio-temporal correlation between them was further explored. It is found that: (1) The average human activity intensity (0.11) in the research area has kept relatively low since this century, and the overall trend has been slowly rising in a stepwise manner (0.0024·a−1); in addition, the increase in human activity intensity has lagged behind that in construction land and population by 1–2 years. (2) The annual average surface temperature in the area is 7.18 °C with a pronounced growth. The rate of change (0.02 °C·a−1) is about 2.33 times that of the world. The striking boost in spring (0.068 °C·a−1) contributes the most to the overall warming trend. Spatially, the surface temperature is low in the south and high in the north, due to the prominent influence of the underlying surface characteristics, such as elevation and vegetation coverage. (3) The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there, showing a strong distribution in the east section and a weak one in the west section. The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities, manifestations, and land-use changes. Vegetation-related human interventions, such as agriculture and forestry planting, urban greening, and afforestation, can effectively reduce the surface warming caused by human activities. This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development. [ABSTRACT FROM AUTHOR]

Human activities have significantly degraded ecosystems and their associated services. By understanding the spatio-temporal variability and drivers of human activity intensity (HAI), we can better evaluate the interactions between human and terrestrial ecosystems, which is essential for land-use related decision making and eco-environmental construction. As the "third pole," the Tibetan Plateau (TP) plays a strong role in shaping the global environment, and acts as an important ecological security barrier for China. Based on land-use/cover change data, environmental geographic data, and socioeconomic data, we adopted a method for converting different land use/cover types into construction land equivalent to calculate the HAI value and applied the Getis-Ord Gi* statistic to analyze the spatio-temporal dynamics associated with HAI since 1980 on the TP. Thereafter, we explored the forces driving the HAI changes using GeoDetector software and a correlation analysis. The main conclusions are as follows: It was observed that HAI increased slowly from 3.52% to 3.65% during the 1980–2020 period, with notable increases in the western part of the Qaidam Basin and Hehuang Valley. Spatially, HAI was associated with a significant agglomeration effect, which was mainly concentrated in the regions of the Yarlung Zangbo and Yellow-Huangshui rivers. Both natural and anthropogenic factors were identified as important driving forces behind the spatial changes in HAI, of which soil type, gross domestic product, and population density had the greatest influence. Meanwhile, the temporal changes in HAI were largely driven by economic development. This information provides crucial guidance for territory development planning and ecological-protection policy decisions. [ABSTRACT FROM AUTHOR]

Human activity intensity is a synthesis index for describing the effects and influences of human activities on land surface. This paper presents the concept of human activity intensity of land surface and construction land equivalent, builds an algorithm model for human activity intensity, and establishes a method for converting different land use/cover types into construction land equivalent as well. An application in China based on the land use data from 1984 to 2008 is also included. The results show that China's human activity intensity rose slowly before 2000, while rapidly after 2000. It experienced an increase from 7.63% in 1984 to 8.54% in 2008. It could be generally divided into five levels: Very High, High, Medium, Low, and Very Low, according to the human activity intensity at county level in 2008, which is rated by above 27%, 16%-27%, 10%-16%, 6%-10%, and below 6%. China's human activity intensity was spatially split into eastern and western parts by the line of Helan Mountains- Longmen Mountains-Jinghong. The eastern part was characterized by the levels of Very High, High, and Medium, and the levels of Low and Very Low were zonally distributed in the mountainous and hilly areas. In contrast, the western part was featured by the Low and Very Low levels, and the levels of Medium and High were scattered in Gansu Hexi Corridor, the east of Qinghai, and the northern and southern slopes of Tianshan Mountains in Xinjiang. [ABSTRACT FROM AUTHOR]