Carbonyl sulfide (COS) is an effective tracer for estimating Gross Primary Productivity (GPP) in the carbon cycle. As the largest contribution to the atmosphere, anthropogenic COS emissions must be accurately quantified. In this study, an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission sources. China’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr⁻¹ from 2015-2021, differing from the trends of other pollutants. Despite an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic, a rapid rebound in emissions occurred following the resumption of economic activities. In 2021, industrial sources, coal combustion, agriculture and vehicle exhaust accounted for 76.8%, 12.3%, 10.5% and 0.4% of total COS emissions, respectively. The aluminum industry was the primary COS emitter among industrial sources, contributing 40.7% of total emissions. Shandong, Shanxi, and Zhejiang were the top three provinces in terms of anthropogenic COS emissions, reaching 39, 21 and 17 Gg S yr⁻¹, respectively. Provincial-level regions (hereafter province) with high COS emissions are observed mainly in the eastern and coastal regions of China, which, together with the wind direction, helps explain the pattern of high COS concentrations in the Western Pacific Ocean in winter. The Green Contribution Coefficient of COS (GCC_COS) was used to assess the relationship between GDP and COS emissions, highlighting the disparity between GDP and COS contributions to green development. As part of this analysis, relevant recommendations are proposed to address this disparity. The COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model (STEM), reducing uncertainties in the atmospheric COS source‒sink budget and promoting understanding of the atmosphere sulfur cycle.

Clarifying the mechanisms that control the evolution of territorial space patterns is essential for regulating and optimizing the geographical structure and processes related to sustainable development. Using the Guangdong and Guangxi sections of the Pearl River Basin as examples, the transfer-matrix method and standard deviation ellipse model were applied to characterize the evolution of territorial space patterns from 1990 to 2020. A trend surface analysis and the Theil index were used to analyze regional differences in the evolution process, and geodetectors were used to identify the underlying mechanisms of the changes. There were three key results. (1) In these critical areas of the Pearl River Basin, agricultural and ecological spaces have rapidly declined due to urban expansion, with transfers between these spaces dominating the evolution of territorial space patterns. Spatial pattern changes in the Guangdong section were more intense than in the Guangxi section. (2) Regional differences in urban space have decreased, whereas differences in agricultural and ecological spaces have intensified. Driven by socio-economic growth, the cross-regional transfers of territorial space have created a “high in the east, while low in the west” inter-regional difference, and a “high in the south, while low in the north” intra-regional difference shaped by natural conditions. The regional differences in space patterns were greater in Guangdong than in Guangxi. (3) The evolution of watershed territorial space patterns resulted from scale changes, locational shifts, structural reorganizations, and directional changes driven by multiple factors. Natural environment, social life, economic development, and policy factors played foundational, leading, key driving, and guiding roles, respectively. Additionally, the regional differences in the evolution of watershed territorial space patterns originated from the differential transmission of the influence of various factors affecting spatial evolution. Enhancing urban space efficiency, restructuring agricultural space, and optimizing ecological space are key strategies for building a complementary and synergistic territorial space pattern in the basin.

Runoff observation uncertainty is a key unsolved issue in the hydrology community. Existing studies mainly focused on observation uncertainty sources and their impacts on simulation performance, but the impacts on changes of flow regime characteristics remained rare. This study detects temporal changes in 16 flow regime metrics from five main components (i.e., magnitude, frequency of events, variability, duration, and timing), and evaluates the effects of observation uncertainty on trends of flow regime metrics by adopting a normal distribution error model and using uncertainty width, significant change rate of slopes, coefficient of variation, and degree of deviation. The daily runoff series from 1971 to 2020 at five hydrological stations (i.e., Huangheyan, Tangnaihai, and Lanzhou in the Yellow River Source Region, Xianyang in the Weihe River Catchment, and Heishiguan in the Yiluo River Catchment) in the water conservation zone of Yellow River are collected for our study. Results showed that: (1) Flow regimes showed significant increases in the low flow magnitude, and significant decreases in the high and average flow magnitude, variability and duration at all the five stations. The magnitude, variability and duration metrics decreased significantly, and the frequency metrics increased significantly at Heishiguan. The low flow magnitude and timing metrics increased significantly, while the high flow magnitude, frequency and variability metrics decreased significantly at Xianyang. The low flow magnitude and high flow timing metrics increased significantly, while the low flow frequency, high flow magnitude and variability metrics decreased significantly in the Yellow River Source Region. (2) Observation uncertainty remarkably impacted the changes of 28.75% of total flow regime metrics at all the stations. The trends of 11.25% of total metrics changed from significance to insignificance, while those of 17.5% of total metrics changed from insignificance to significance. For the rest metrics, the trends remained the same, i.e., significant (18.75%) and insignificant (52.50%) trends. (3) Observation uncertainty had the greatest impacts on the frequency metrics, especially at Xianyang, followed by duration, variability, timing and magnitude metrics.

Studying runoff characteristics and quantifying human activities’ impact on northern Shaanxi, a crucial mineral resource area in China, is crucial to alleviate water resource contradictions. In this study, hydrological element trends were analyzed using the β-z-h three-parameter indication method. The Mann-Kendall, Pettitt, moving T, and Yamamoto methods were used to test the mutation point of hydrological elements. The Budyko framework was used to quantitatively assess the impacts of climate change and multiple human activities on runoff reduction. The results showed that (1): Precipitation (PRE), potential evapotranspiration (E₀), and temperature (TEM) showed increasing trends; runoff in the Huangfuchuan, Gushanchuan, Kuye River, Tuwei River, Wuding River, Qingjian River, and Yanhe River catchments showed decreasing trends (HFC, GSC, KYR, TWR, WDR, QJR, YR); whereas runoff in the Jialu River (JLR) catchment showed a “V-shaped” trend from 1980 to 2020. (2) Runoff was positively correlated with PRE and negatively correlated with E₀ and the subsurface index (n), with the elasticity coefficients of PRE, E₀, and n showing an increasing trend in the change period. (3) Human activities were a key factor in runoff reduction, although the impact of different human activities showed spatial variations. This study provides a scientific foundation for achieving the sustainable development of water resources in mining areas.

Land use sustainability is a pivotal concern in contemporary ecological protection efforts, necessitating a comprehensive understanding of the ramifications of changes in land use intensity (LUI) on ecosystem services (ESs). Although ecological control zoning typically emphasizes ES outcomes, it tends to overlook the impacts of human activity intensity. This research focuses on the Yellow River Basin and integrates various data sources, encompassing land use, meteorological, soil, and socioeconomic data from 1980 to 2020. Using the InVEST model, quadratic polynomial fitting, and cluster analysis, this work evaluates the spatiotemporal changes and zoning characteristics of LUI and three ESs—water yield, soil conservation, and habitat quality—to explore the influence of LUI changes on ESs. The results indicate that from 1980 to 2020, LUI shows a sustained increase with considerable spatial heterogeneity, gradually intensifying from upstream to downstream areas. The interannual variability of ESs is minimal, with substantial local fluctuations but overall minor changes. LUI correlates positively with ESs. Based on regional ESs, the Yellow River Basin is categorized into four primary ecological function zones: ecological restoration, ecological pressure, ecological sustainability, and ecological conservation. Considering LUI characteristics, this categorization is further refined into six secondary function zones: ecological restoration, ecological transition, ecological overload, potential development, eco-economic carrying, and ecological conservation. This study provides a scientific foundation for land use planning and ecological conservation policy formulation within the watershed area.

Shaanxi province in China is rich in vegetation resources, making the study of vegetation phenology crucial for understanding climate change and ecological dynamics. Previous studies on Shaanxi have primarily focused on coarse-scale phenology, which results in the loss of essential spatial details due to the lack of finer-scale analyses. Moreover, the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear. To address these gaps, we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence. Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed, and the responses of different vegetation types to urbanisation were explored. Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution, which is more precise than conventional coarse-scale investigations. The Start of Season (SOS) and End of Season (EOS) in the province advanced by −0.56 and −0.08 day/year, respectively, from 2000 to 2020. In general, the SOS gradually delays, and the EOS progressively advances with increasing latitude. However, anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N, which are primarily due to the effects of high elevation and rapid urbanisation, respectively. Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS, while grasslands present the latest. In addition, a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient, with urbanisation having the most significant impact on cropland phenology. This study provides valuable insights into vegetation phenology and its response to urbanisation. These insights offer crucial information for ecological conservation and management strategies.

Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities. Nevertheless, the inherent heterogeneity in multimodal migration big data has been ignored. This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association. Initially, the intercity interactive networks in China were constructed, utilizing migration data from Baidu and AutoNavi collected during the same time period. Subsequently, the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall (network) and local (node) perspectives. Furthermore, the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone (MP) data. Results indicate that the intercity interactive networks in China, as delineated by Baidu and AutoNavi migration flows, exhibit a high degree of structure equivalence. The correlation coefficient between these two networks is 0.874. Both networks exhibit a pronounced spatial polarization trend and hierarchical structure. This is evident in their distinct core and peripheral structures, as well as in the varying importance and influence of different nodes within the networks. Nevertheless, there are notable differences worthy of attention. Baidu intercity interactive network exhibits pronounced cross-regional effects, and its high-level interactions are characterized by a “rich-club” phenomenon. The AutoNavi intercity interactive network presents a more significant distance attenuation effect, and the high-level interactions display a gradient distribution pattern. Notably, there exists a substantial correlation between the AutoNavi and MP networks at the local scale, evidenced by a high correlation coefficient of 0.954. Furthermore, the “spatial dislocations” phenomenon was observed within the spatial structures at different levels, extracted from the Baidu and AutoNavi intercity networks. However, the measured results of network spatial structure similarity from three dimensions, namely, node location, node size, and local structure, indicate a relatively high similarity and consistency between the two networks.

The exploration of the variability of spatial spillovers of ecosystem services (ESs) across scales is essential for sustainable regional development. Using advanced models such as InVEST, Geodetector, MGWR, and SLM/SEM/SDB, this study investigates spatial heterogeneity and cross-regional effects on ESs at the raster and county scales. Key findings from 2000 to 2020 include an upward trend in ESs, with pronounced regional variations. The southern Yellow River Basin (YRB) shows higher levels of water yield, carbon sequestration, soil retention, and habitat quality, while the northern areas score lower, except for food provisioning in central and lower regions. The overall ES index has risen, particularly in the southern part, aligning with China’s ecological patterns and showing significant cross-regional benefits. At various scales, natural elements, landscape configurations, and human influences significantly impact ESI, with different cross-regional effects. While natural and landscape indices demonstrate substantial cross-regional impacts at the raster scale, human influence is more apparent at the county scale. The identified cross-regional impacts underscore the interconnectedness of regional ES and sustainability, extending to nearby areas. Spatial management and planning may be limited by zoning and regulations. This study underscores regional ecosystem spatial spillovers and cross-scale knowledge differences and linkages, introducing new perspectives and methods for spatial planning in watersheds to support sustainable ecosystem optimisation.

Under global warming, understanding the impact of urbanization on the characteristics of different heatwaves is important for sustainable development. In this study, we investigated the changes of heatwaves characteristics in the Yangtze River Delta urban agglomeration (YRDUG) and analyzed the influencing mechanisms of urbanization. Results showed that: (1) the duration, frequency, and intensity of NHWs (Nighttime Heatwaves) and CHWs (Daytime-nighttime compound Heatwaves) had shown a significant increase and the CHWs showed the greatest increasing trend. Furthermore, the NHWs exhibited higher durations, frequencies, and intensities compared to DHWs (Daytime Heatwaves); (2) Since 1990, the DHWs and CHWs were greater in urban areas than in rural areas, NHWs had been more pronounced in rural areas than in urban centers; and (3) Cloud cover, solar radiation, etc. affected heatwaves. Furthermore, in the process of urbanization, the increase in impervious area and the decrease in green land exacerbated heatwaves. Considering the combined effect of DHWs and NHWs, CHWs continued to increase.