Xi\'an is the political, economic, and cultural center of northwest China with a developed industry. Air pollution incidents have brought great challenges to the high-quality development of the social economy. It is vital to study air pollution characteristics and clarify their impact on human health. In this study, we first analyzed the spatiotemporal variations in air pollutants in the study region from 2015 to 2021. Then, the air quality index (AQI), aggregate air quality index (AAQI), and health risk-based air quality index (HAQI) were used to assess health risks. Based on these, the AirQ2.2.3 model was used to quantify health effects. The results showed that the major pollutants were PM10, PM2.5, and O3. The main pollution characteristics of the study area were terrain characteristics and the mixed pollution of anthropogenic emissions. Compared to that of AQI, AAQI and HAQI showed better classification performance for pollution levels. HAQI revealed that approximately 80 % of the population was exposed to unhealthy air throughout the year in the study region. People were most exposed to unhealthy air in winter, followed by autumn and spring, and the least in summer. The AirQ2.2.3 model quantified the total mortality proportions attributable to PM2.5, PM10, SO2, CO, NO2, and O3, which were 0.99 %, 2.04 %, 0.41 %, 1.72 %, 8.76 %, and 3.67 %, respectively. The attributable proportion of mortality of the respiratory system and cardiovascular diseases was consistent with the change rule of total mortality.

Ongoing rapid urbanization has triggered significant changes in land use, rendering landscape patterns adversely impacted and certain habitat patches degraded. Ecological networks have consequently contracted overall. As such, an investigation into how land-use landscape patterns and ecological networks change over time and space is of major significance for ecological restoration and regional sustainability. Taking Xuzhou Planning Area as a case study, we examined spatiotemporal changes and features of the landscape pattern by employing the land-use change degree, the land-use transition matrix, and quantified landscape pattern indices. An ecological network analysis, which studies the changes in network connectivity and robustness, as well as their causes and contributors, was undertaken to probe into the features and trends of spatiotemporal changes in the land-use landscape pattern and ecological network amid expeditious urbanization. Analysis results unveiled the following: (1) From 1985 to 2020, there was a decline in the area of farmland, forest, and grassland, accompanied by an increase in land for construction, water bodies, and unused land. The southwestern research area witnessed farmland substantially give way to land for construction for this period, and the most dramatic change in land use occurred between 2000 and 2010. (2) The area of dominant patches in the research area shrank, along with more fragmented, complex landscapes. The land for construction was emerging as the dominant landscape by area, whereas patches of farmland, forest, grassland, and water bodies became less connected. (3) The ecological network was densely linked in the northeast, with sparser connections in the southwest. Spatial shrinkage was observed in the research area\'s southwestern and central ecological corridors. Overall, the number of ecological sources and corridors rose and subsequently dropped before a rebound. (4) The ecological network grew more connected and robust from 1985 through 1990, as portions of farmland were converted into water bodies, which led to an increase in ecological sources. Given a reduction in ecological sources and corridors in the southwestern and central regions between 1990 and 2010, network connectivity and robustness declined, which was reversed from 2010 onward with the addition of two ecological sources-Pan\'an Lake and Dugong Lake. With an optimal ecological network in 1990, however, it deteriorated significantly by 2010. The research area saw the minimum value of its network connectivity indices of network stability index (α), evenness index (β), and connectivity index (γ), in 2010, when its ecological network was highly fragmented and vulnerable, attributing to a strong contrast between the maximal connected subgraph\'s relative size and connectivity robustness. The research findings can lay scientific groundwork for addressing ecological issues, restoring landscape patterns, and developing ecological networks amid urbanization.

Wetlands, known as the \"kidney of the earth\", are an important component of global ecosystems. However, they have been changed under multiple stresses in recent decades, which is especially true in the Yellow River Delta. This study examined the spatiotemporal change characteristics of wetlands in the Yellow River Delta from 1980 to 2020 and predicted detailed wetland changes from 2020 to 2030 with the patch-generating land use simulation (PLUS) model under four scenarios, namely, the natural development scenario (NDS), the farmland protection scenario (FPS), the wetland protection scenario (WPS) and the harmonious development scenario (HDS). The results showed that wetlands increased 709.29 km2 from 1980 to 2020 overall, and the wetland types in the Yellow River Delta changed divergently. Over the past four decades, the tidal flats have decreased, whereas the reservoirs and ponds have increased. The gravity center movement of wetlands differed among the wetland types, with artificial wetlands moving to the northwest and natural wetlands moving to the south. The movement distance of the gravity center demonstrated apparent phase characteristics, and an abrupt change occurred from 2005 to 2010. The PLUS model was satisfactory, with an overall accuracy (OA) value greater than 83.48 % and an figure of merit (FOM) value greater than 0.1164. From 2020 to 2030, paddy fields and tidal flats decreased, whereas natural water, marshes and reservoirs and ponds increased under the four scenarios. The WPS was a relatively ideal scenario for wetlands, and the HDS was an alternative scenario for wetland restoration and food production. In the future, more attention should be paid to restoring natural wetlands to prevent further degradation in the Yellow River Delta. This study provides insights into new understandings of historical and future changes in wetlands and may have implications for wetland ecosystem protection and sustainable development.

Future phosphorus (P) shortages could seriously affect terrestrial productivity and food security. We investigated the changes in topsoil available P (AP) and total P (TP) in China\'s forests, grasslands, paddy fields, and upland croplands during the 1980s-2010s based on substantial repeated soil P measurements (63,220 samples in the 1980s, 2000s, and 2010s) and machine learning techniques. Between the 1980s and 2010s, total soil AP stock increased with a small but significant rate of 0.13 kg P ha-1  year-1 , but total soil TP stock declined substantially (4.5 kg P ha-1  year-1 ) in the four ecosystems. We quantified the P budgets of soil-plant systems by harmonizing P fluxes from various sources for this period. Matching trends of soil contents over the decades with P budgets and fluxes, we found that the P-surplus in cultivated soils (especially in upland croplands) might be overestimated due to the great soil TP pool compared to fertilization and the substantial soil P losses through plant uptake and water erosion that offset the P additions. Our findings of P-deficit in China raise the alarm on the sustainability of future biomass production (especially in forests), highlight the urgency of P recycling in croplands, and emphasize the critical role of country-level basic data in guiding sound policies to tackle the global P crises.

Desertification is a major manifestation of land degradation in China. The monitoring and assessment of land desertification in China and the analysis of its driving mechanisms are crucial to the realization of the aspiration of \"net zero land degradation\" proposed by the United Nations Convention to Combat Desertification (UNCCD). An improved Mediterranean Desertification and Land Use (MEDALUS) model was applied to assess the multiyear spatial distribution of land desertification sensitivity across China in 2010, 2015, and 2020. A Principal Component Analysis (PCA) was used to evaluate the internal stability of the model. In addition, a Geographical Detector method was used to examine the driving mechanisms of desertification sensitivity in China. The results showed that extremely sensitive desertification areas were primarily concentrated within the Northwest Desert and Desertification Region, northern segment of the Qinghai-Tibet Plateau desert and desertification region, and western sector of the Inner Mongolia-Daxinganling Desert and Desertification Region. In addition, the proportion of land area showing an overall reduction in sensitivity (17.07 %) exceeded that showing an increase (16.56 %). This indicates an overall diminishing trend in sensitivity to land desertification across China. Land use intensity (LUI), drought resistance (DR), erosion protection (EP), and aridity index (AI) are consistently the most important drivers. From 2015 to 2020, the LUI emerged as the principal catalyst behind the transformation of land desertification sensitivity in China. Hence, emphasizing well-planned land use is vital for ensuring harmony between land utilization and ecological capacity. This study establishes a scientific basis for China\'s land desertification control strategy and serves as a quantitative analysis reference for the driving mechanisms.

Climate warming can increase soil temperature and lead to soil carbon release, but it can also increase soil organic carbon by increasing primary productivity. Cropland soils are considered to have a huge potential to sequester carbon; however, direct observations for the responses of cropland soil organic carbon to climate warming over broad geographic scales are rarely documented. Paddy soil is one of the important cultivated soils in China. Based on the data of 2217 sampling points obtained during the second national soil survey and the data of 2382 sampling points collected during 2017-2019, this study analyzed the change characteristics of soil organic carbon content of paddy surface soil in Sichuan Basin of China and explored the relationships between the soil organic carbon change of paddy soil and temperature, precipitation, cropland use type, fertilization intensity, and grain yield. The results showed that the content of soil organic carbon of paddy soil changed from 13.33 g·kg-1to 15.96 g·kg-1, with an increase of 2.63 g·kg-1, suggesting that soils in the Sichuan Basin have acted as a carbon sink over past 40 years. The soil organic carbon increment of paddy soil varied with different geomorphic regions and different secondary basins. The increase in SOC content in paddy soil was positively correlated with annual average temperature; negatively correlated with annual average precipitation; and initially increased and then decreased with annual average fertilizer application, annual average increase rate of fertilizer application, annual average grain yield, and annual average grain yield growth rate. The relationship between the increase in SOC content and the annual average temperature growth rate was different under different farmland utilizations, and the increase in the annual average temperature growth rate had significant effects with the increase in SOC content only on paddy-dryland rotation. These results indicate that the paddy soil organic carbon change in Sichuan Basin was co-affected by various factors, but climate warming was an important factor leading to the paddy soil organic carbon change, and its influence was controlled by the water conditions determined by farmland use.

The Mexican Caribbean coral reef ecosystem has endured the effects of global and regional stressors and, recently, the massive arrivals of the free-living, floating brown algae Sargassum spp. This study aimed to evaluate spatiotemporal changes in the stony coral community structure in the southern Mexican Caribbean by a temporal comparison of live coral cover and colony density using a data set collected in 2008-2009 and a recent survey in 2021 within a Protected Natural Area. A multivariate analysis approach was used to reveal spatiotemporal changes in coral cover and colony densities. Coral cover ranged from 6.9 to 8.9% in 2008-2009 to 6.5% in 2021, the lowest values recorded for the area. Coral colony density ranged from 0.68 to 0.78 colonies m-1 in 2008-2009 to 0.68 colonies m-1 in 2021. The present results appear to represent subtle changes during the last decade.

Understanding the spatiotemporal variations in climate extremes indices, as well as the influencing factors, is critical to the scientific response to climate change. The temporal and spatial variations of SU25 (annual count of days when daily maximum temperature > 25 °C) were discussed in this study, based on daily maximum temperature data from 2398 meteorological stations in China from 1961 to 2017. The contributions of associated large-scale circulation factors to SU25 were quantitatively assessed by using the geographical detector method (GMD). The overall spatial distribution of SU25 was marked by a considerable increase from north to south. The SU25 increased significantly over time, with the national SU25 increasing at a rate of 2.5 days/decade. The Tibet Plateau (TP) had the slowest growth rate, with an average increase rate of 1.4 days/decade. The Hurst values of SU25 in all the subregions were generally high, indicating that most stations of SU25 would continue to increase in the future. Except for TP, the tipping years of other subregions were concentrated in the 1990s, and SU25 increased after the years. Among the large-scale circulation factors affecting SU25 in each subregion, Atlantic Multidecadal Oscillation (AMO) played a major role in SU25 variability. As a whole, the result of the pairwise interaction of each circulation factor was mainly nonlinear enhancement. The joint contributions of multiple factors to SU25 were larger than the contribution of each individual factor.

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km2, 271.7136 km2, 40.0104 km2, and 109.2483 km2, respectively, whereas grassland and bare land decreased by 677.7243 km2 and 598.5945 km2, respectively; (2) Kappa coefficients reflect the accuracy of the mode\'s predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA-Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.