The spatial and temporal characteristics of land use carbon emissions are relevant to the sustainable use of land resources. Although spatial and temporal studies have been conducted on land use carbon emissions, the spatial correlation of land use carbon emissions at the city level still requires further research. Here, we estimated the distribution of carbon emissions at the city level in Shandong Peninsula urban agglomeration in spatial and temporal terms based on land use remote sensing data and fossil energy consumption data during 2000–2019. The results showed that the land use change in the 16 cities in the study area was the conversion of cropland to construction land. Carbon emissions from land use had an upward trend for all 16 cities overall during the period of 2000–2019, but the incremental carbon emissions trended downward after 2010. Among them, Jinan and Qingdao had higher carbon emissions than other cities. In addition, we also found that land use carbon emissions at the city level were characterized by stochasticity, while per capita carbon emissions displayed geospatial aggregation. Among them, Yantai displayed a spatial pattern of high–high clustering of carbon emissions, while Jining presented a spatial pattern of low–low clustering in terms of land-average carbon emissions and carbon emissions per capita during 2000–2019. The results of the study are important for guiding the achievement of urban carbon emission reduction and carbon neutrality targets at the city level.

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.

The Eurasian steppe (EAS) is the largest contiguous grassland worldwide. Quantitative evaluations of the relative impacts of climate change and human activities on grasslands are significant for understanding grassland degradation mechanisms and controlling degraded grasslands. In this study, we analyzed the grassland productivity based on multiple forms of net primary productivity (NPP), including climate NPP (CNPP), actual NPP (ANPP), and human-caused NPP (HNPP) during 2000–2014. The results demonstrate that the average value of annual ANPP in the EAS was 47.36 gC/(m²·year), with a weak decrease (−0.02 gC/(m²·year)) during the study period. The area of grassland degradation account for 48.52% of the total grassland area in EAS, while the area of grassland recovery account for 51.48%. Restorative grassland was mainly distributed in Mongolia and China, while worse grassland was mainly distributed in the Kazakh steppe regions. Grassland degradation in China was mainly caused by climate change, whereas it was mainly caused by human activities in Mongolia. Grassland recovery in Kazakh steppe regions was mainly caused by human activities, but in Mongolia, it was mainly caused by climate change. Compared with temperature, precipitation played a more significant role on grassland productivity.

Groundwater is an essential water source for drinking, domestic, irrigation and industrial production in Luxi Plain, Shandong Province, China. Understanding the spatial–temporal changes in groundwater quality and its influencing factors in the region were required for better utilization of groundwater resources and efficient design of groundwater management strategies. In this study, the hydrochemical characteristics of groundwater in the study area were analyzed, and significant evolution was found from 2018 to 2020 due to silicate and carbonate weathering, evaporation and human activities. Moreover, the entropy water quality index (EWQI) was used to assess groundwater quality from 2018 to 2020. The EWQI values in 2018–2020 were 129.5, 90.5 and 94.0, respectively, and 31.7% of the groundwater in 2019 and 20.0% in 2020 can be used directly for drinking in the study area; others can be used for domestic water or irrigation. The potable groundwater, with an EWQI value of <50 (ranked as class Ⅰ or Ⅱ water quality), was mainly distributed in the west and southeast of the study area. The potential health risk due to oral intake and dermal intake was further assessed based on the human health risk assessment (HHRA) model. The results showed that, 37.3%, 6.7% and 3.3% of the groundwater samples for adults exceeded the acceptable limit for non-carcinogenic risk of 1.0 in 2018–2020, while for children, they were 88.2%, 30.0% and 56.7%, respectively. The high non-carcinogenic risks virtually all occurred in the counties or districts with higher agricultural or economic values. This work may provide useful information for local groundwater conservation and management and help to ensure a sustainable and healthy water supply for drinking, domestic and agricultural needs.

This study applied grassland related multi-index and assessed the effects of climate change by investigating grassland responses to drought. This process was performed to study grassland vegetation dynamic accurately and evaluate the effect of drought in the Mongolian Plateau (MP). The spatial–temporal characteristics of grassland dynamic in terms of coverage (F_v), surface bareness (F_b), and net primary production (NPP) from 2000 to 2013 were explored. We implemented the maximum Pearson correlation to analyze the grassland vegetation in response to drought by using self-calibrating Palmer Drought Severity Index (scPDSI). Results show that F_v and NPP present an increasing trend (0.18 vs. 0.43). F_b showed a decreasing trend with a value of −0.16. The grassland F_v and NPP positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, and F_b was −0.08. The positive correlation between F_v and NPP accounted for 84.08%, and the positive correlation between F_v and scPDSI accounted for 93.88%. On the contrary, the area with a negative correlation between F_b and scPDSI was 57.43%. The grassland in the MP showed a recovery tendency. The increase in grassland caused by positive reaction was mainly distributed in the middle of Mongolia (MG), whereas that caused by counter response was mainly distributed in the east and west MG and northeast Inner Mongolia autonomous region of China (IM). The relevant results may provide useful information for policymakers about mitigation strategies against the inverse effects of drought on grassland and help to ease the losses caused by drought.

Educational tourism serves as a vital avenue for enhancing students’ practical skills and knowledge acquisition. As key components of educational tourism, the spatial distribution and accessibility of educational tourism bases significantly influence the effectiveness of study tour programs. Here, we employed stepwise regression analysis and geographically weighted regression (GWR) to analyze the spatial distribution characteristics, accessibility, and driving factors of educational tourism bases along the Yellow River region. The results indicate that museums accounted for the greatest proportion at 25.14% of the nine types of educational tourism bases. The educational tourism bases show a pattern of “dense in the southeast and sparse in the northwest.” Furthermore, they demonstrate clustered distributions centered around provincial capitals. The key factors influencing the distribution of educational tourism bases, ranked from highest to lowest based on the absolute value of the GWR coefficient, are as follows: 4A and above scenic spots > urbanization rate > internet broadband users > length of classified roads > education expenditure. The accessibility of educational tourism bases is basically consistent in terms of time and spatial distance, both showing better accessibility in the southeast than in the northwest. However, where Sichuan Province and Gansu Province meet, time accessibility is better, while distance accessibility is worse. These findings provide valuable insights for promoting the sustainable development of educational tourism in the Yellow River region.