Spatial-temporal Changes Research Articles

Modeling spatial-temporal variability of PM2.5 concentrations in Belt and Road Initiative (BRI) region via functional adaptive density approach.

The rapid development of the Belt and Road Initiative (BRI) has led to severe air pollution dominated by PM2.5 concentrations which can cause a profound negative impact on human health and economic activity. This problem poses a critical environmental challenge to efficiently handling large-scale spatial-temporal PM2.5 data in this extended region. Functional data analysis (FDA) technique offers powerful tools that have the potential to enhance the analysis of spatial distributions and temporal dynamic changes in high-dimensional pollution data. However, modeling the spatial-temporal variability of PM2.5 concentrations by FDA remains unrevealed in the BRI region. To address this research gap, our study aimed to achieve two main objectives: first, to model the spatial-temporal dynamic variability of PM2.5 in 125 BRI nations (1998-2021), and second, to identify the underlying clusters behind the variations. We employed the recently developed functional adaptive density peak (FADP) clustering approach to solve the current problem. The proposed method is based on the joint use of functional principal components (FPCs) and functional cluster analyses. The main results are as follows: (i) The first three FPCs almost captured 99% of the total variations involving all valuable information on PM2.5 concentrations. (ii) PM2.5 pollution was highly concentrated in the developing countries (Pakistan, Bangladesh, and Nigeria) and the developed countries (Arabian Gulf countries: Qatar, United Arab Emirates, Bahrain, Saudi Arabia, Oman), and the least developed countries (Yemen and Chad). (iii) Three optimal clusters were identified and thus classified the PM2.5 into three distinct degrees of pollution: severe, moderate, and light. (iv) Cluster 1 had a severe pollution effect degree with a high rate of change, and it covered the Arabian Peninsula countries, African countries (Cameroon, Egypt, Gambia, Mali, Mauritania, Nigeria, Sudan, Senegal, Chad), Bangladesh, and Pakistan. (v) About 62 BRI countries belonged to cluster 2 showing a light pollution degree with annul average of less than 20 [Formula: see text]; this pointed out that the PM2.5 concentration remains stable in the cluster 2-related countries. The findings of this research would benefit governments and policymakers in preventing and controlling PM2.5 pollution exposure in BRI. Furthermore, this research could pay attention to sustainable development goals and the vision of the Green BRI policy.

Air quality impacts of war detected from the Sentinel-5P satellite over Ukraine

The general aim of this research is to analyze spatial-temporal changes of air quality, which are the consequences of the military invasion of the Russian on the territory of Ukraine based on open remote sensing data, geoinformation and cloud technologies. The density of NO2, SO2, CO and HCHO as well as the density of O3 and the absorbing aerosol index (AAI) extracted from the Sentinel-5P satellite using Google Earth Engine (GEE) showed reduced emission of primary air pollutants and a high level of atmospheric restoration compared to 2021, the year before the war. At the same time values of the AAI increased significantly, indicating the presence of smoke and dust in the air. Obtained results showed one more aspect of the drastic effect of the war on the economic and social processes and environmental conditions. The consequences of this ecological effect can have a long-term negative impact on the health of populations.

Assessment of the current state of the hydrochemical regime and water-environmental problems of the basin of the Gavaraget River flowing into Lake Sevan

The results of studies of the current hydro-chemical state and water-ecological problems of the Gavaraget River basin, which flows into Lake Sevan from the north-west, are presented. Extreme water discharge, ten-day flow, ecological output have been determined, water-ecological features have been identified, long-term dynamics of the main indicators characterizing the ecological state of the Gavaraget River, spatial-temporal changes in winter and summer-autumn ten-day flow have been shown, the hydro-chemical state has been presented, the main water-ecological problems of this basin and ways to solve them were proposed, the physical and chemical state of Gavaraget water was clarified in accordance with the standards and requirements for water quality, and the level of risk was indicated. It has been clarified that the annual runoff in the basin basically follows the general features of the annual precipitation regime: the minimum amount falls in winter, and the maximum in spring. It is shown that Gavaraget is one of the most polluted rivers in the Lake Sevan basin. It was revealed that it is mainly contaminated with untreated domestic wastewater and agricultural return water. It was found that as the impact of anthropogenic load increases, river water pollution increases from the source to the mouth of the river. The water quality in the upper part of Gavaraget (0.5 km upstream from the village of Tsaghkashen) is excellently characterized as “excellent” (I) and “good” (II), in the lower part (at the river mouth) with “moderate” (III) and “unsatisfactory” (IV) class. The research results can be useful in solving geo-ecological problems, managing aquatic ecosystems, determining ecological flow, assessing risks, predicting water disasters, protecting river ecosystems from degradation, etc.

Spatial-temporal pattern evolution and geological influence factors analysis of ecological vulnerability in Western Sichuan mountain region

Compared to plains, hills and basins, mountainous areas with complex topography are more prone to ecological and environment vulnerability to climate change, human interference and geological disasters. This type of region is usually located in abrupt transitional zones, which marked by active neotectonic movements, high-incidence mountain disasters, abundant available resources, slower economic development, diverse eco-environment and insufficient cultivated land resources. Therefore, it is of great significance to determ the vulnerability status and spatial–temporal change process of the eco-environment in mountainous area with complex topography. In this study, data derived from vegetation, topography, soil, meteorological and human social factors were used to develop an evaluation index system that can reflect the characteristics of the eco-environment in the mountain area. Employing the Genetic Projection Tracing Model (RAGA-PPM), we developed a framework for assessing the vulnerability of mountainous areas with complex terrains and applied it to the western Sichuan mountain region, China. In addition, the relationship between the geological factors and the vulnerability of eco-environment was quantitatively analyzed by using the geographic detector. The results indicate that (1) The overall vulnerability was medium and the spatial distribution was higher in the central and eastern parts and lower in the surrounding areas. The area with high vulnerability accounted for 27.9%. (2) The overall degree of vulnerability increased from2010 to 2020, the eco-environment experienced a rapid deterioration from 2010 to 2015, followed by a slower improvement from 2015 to 2020. (3) Correlations were minimal between vulnerability results and fault density and earthquake dynamic peak acceleration, the influence of other geological factors ranged from 10% to 25.8%, indicating a nonlinear correlation. (4) This study reveals that the ecological vulnerability of mountain areas was the effect of multiple human and natural factors, and there was a certain correlation between geological environment and vulnerability distribution. These findings can assist decision-makers in providing a scientific basis for managing and developing the ecological environment in mountainous regions.

The putatively high-altitude adaptation of macaque monkeys: Evidence from the fecal metabolome and gut microbiome.

Animals living in high-altitude environments, such as the Tibetan Plateau, must face harsh environmental conditions (e.g., hypoxia, cold, and strong UV radiation). These animals' physiological adaptations (e.g., increased red cell production and turnover rate) might also be associated with the gut microbial response. Bilirubin is a component of red blood cell turnover or destruction and is excreted into the intestine and reduced to urobilinoids and/or urobilinogen by gut bacteria. Here, we found that the feces of macaques living in high-altitude regions look significantly browner (with a high concentration of stercobilin, a component from urobilinoids) than those living in low-altitude regions. We also found that gut microbes involved in urobilinogen reduction (e.g., beta-glucuronidase) were enriched in the high-altitude mammal population compared to the low-altitude population. Moreover, the spatial-temporal change in gut microbial function was more profound in the low-altitude macaques than in the high-altitude population, which might be attributed to profound changes in food resources in the low-altitude regions. Therefore, we conclude that a high-altitude environment's stress influences living animals and their symbiotic microbiota.

Major contributions of agricultural management practices to topsoil organic carbon distribution and accumulation in croplands of East China over three decades

Multiple natural and anthropogenic factors jointly drive the spatial distribution of soil organic carbon (SOC) and its dynamics in croplands. Among these factors, agricultural management practices have caused considerable impacts. Previous studies on the driving factors of SOC in croplands have provided significant understanding on this matter. However, whether and how the effects and interplay of these drivers change over time is often unknown, especially agricultural activities. To measure the effects of agricultural management practices on the spatial distribution and temporal change of SOC incorporating the network relationships with other natural drivers at the regional scale, we conducted partial least squares path analysis on the topsoil organic carbon content using two historical soil datasets from cropland samples in East China obtained during the 1980s and the 2010s. Eight indicators and their temporal changes reflecting climate, agricultural management, and edaphic conditions were used to quantify the driving mechanisms of the spatial distribution and temporal change of SOC. The drivers of SOC distribution showed that high SOC was mostly distributed in soils with a relatively low pH and high clay content in warm humid climates. High SOC was associated with the application of N fertilizer, crop residue input, and agricultural machinery in the 1980s and 2010s. The effect of N fertilization on SOC distribution increased from the 1980s to the 2010s, whereas the total edaphic effects significantly decreased from 0.62 to 0.25 (P < 0.01). Regarding the drivers of SOC change over the three decades, the edaphic effects presented the strongest effect (path coefficient of 0.74, P < 0.01), including the negative effects of topsoil acidity and baseline SOC level, as well as the positive effects of total nitrogen (TN) and clay content change. Croplands with lower intensity of management practices in the 1980s generally attained more development in agricultural modernization, which led to a considerable SOC increase. Our research revealed the importance of agricultural management practices on the spatial distribution and temporal change of cropland SOC at the regional scale. The results emphasize the need to measure the changing driving mechanisms of SOC dynamics. The findings indicate that indicators reflecting the effects of agricultural management practices should be included in digital mapping and process-based modeling of soil carbon at different spatiotemporal scales to improve prediction accuracy.

Will the ‘evapotranspiration paradox’ phenomenon exist across China in the future?

AbstractThe uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the ‘evapotranspiration paradox’ phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future ‘evapotranspiration paradox’ phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the ‘evapotranspiration paradox’ phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I ‘evapotranspiration paradoxes’ that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I ‘evapotranspiration paradox’ phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat ‘evapotranspiration paradox’‐related extremes at regional scales.

Population dynamics in Taiwan from the Neolithic to early historic periods (5000–100 cal BP): Linking cultural developments and environmental change

To reconstruct population dynamics in Taiwan between 5000 and 100 cal BP, we analysed spatial-temporal changes in archaeological site abundance and size (area). The results suggest that population growth was discontinuous throughout the study region and that the main spatial differences in population trends were between eastern/northern and western Taiwan. Comparison of settlement data shows that population trends in Taiwan and different regions in continental China were partly parallel. We contrast these data with political, economic, settlement, and climatic developments in continental China, when considering possible factors that influenced these demographic relationships. In addition, we examined published palaeoenvironmental time series from the broader study region to explore human-environment interactions. This revealed that proxy records of human activities (agriculture, deforestation) correlate with the derived trends in site abundance and area. Furthermore, we hypothesise that the inferred sparse settlement in eastern Taiwan during 2700–1800 cal BP is related to climate change. An increase in seasonal rainfall and high-intensity precipitation events in late summer and autumn, due to long-term changes in summer monsoon precipitation and El Niño–Southern Oscillation intensity, likely resulted in unfavourable hydrological conditions for crop cultivation in this part of the study region.

Analysis of Spatial-Temporal Changes and Driving Factors of Vegetation Coverage in Jiamusi City

This study of vegetation coverage in Jiamusi City provides theoretical support for local urban development, land use, and ecological environmental protection. Based on the land cover data and Landsat remote sensing image data from 2000 to 2020, the vegetation cover and land use data of Jiamusi City were extracted. The study includes the following aspects: (1) an analysis of the spatio-temporal changes in vegetation coverage; (2) analysis of the land use situation in Jiamusi City; and (3) investigation of the impact of natural and human factors on vegetation coverage in Jiamusi City using the Geodetector model. The results show that (1) over the past 20 years, the vegetation coverage of Jiamusi has shown a decreasing trend, declining from 25.22% in 2000 to 17.13% in 2020, representing a decrease of 32%. In terms of spatial distribution, the areas of Fuyuan City and Tongjiang City have experienced more significant decreases in vegetation coverage, decreasing by 73.6% and 54.0%, respectively. (2) The land use pattern of Jiamusi City has undergone significant changes during the study period; except for paddy fields (PF), unused land (UL), and construction land (CL), the areas of all the land categories have decreased. The ranking of the single land use dynamic degree in terms of magnitude is as follows: PF, UL, CL, dry farmland (DF), vegetation coverage land (VCL), and wetland (WET). (3) The changes in vegetation coverage were influenced by both natural and human activities and, according to the Geodetector results, the main influencing factors were CL and DF. The key findings of this study emphasize the need for comprehensive land use planning and ecological environmental protection that focus on sustainable development and conservation practices, and lay the groundwork for future ecosystem management and urban planning efforts in Jiamusi City.

Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning

Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.

Compound events of wet and dry extremes: Identification, variations, and risky patterns

Compound hydrometeorological extremes have been widely examined under climate change, they have significant impacts on ecological and societal well-being. This study sheds light on a new category compound of contrasting extremes, namely compounding wet and dry extremes (CWDEs). The CWDEs are characterized as devastating dry events (EDs) accompanied by wet extremes (EWs) in a given time window. Notably, we first adopt a separate system to identify coinciding events considering the different evolving processes and impacting patterns of EDs and EWs. The peak-over-threshold and standardized index methods are used in a daily and monthly window to identify EWs and EDs respectively. Furthermore, the spatial-temporal changes and risky patterns of CWDEs are revealed by using the Mann-Kendall test, the Ordinary Least Squares, and the Global and Local Moran indices. Germany is the study case. As one major finding, the results indicate a pronounced seasonal effect and spatial clustering pattern of CWDEs. The summer is the most vulnerable period for CWDEs, and the spatial hotspots are mainly located in the southern tip of Germany, as well as in the vicinity of the capital city Berlin. Besides, robust uptrends in CWDEs across all evaluation metrics have been discovered in historical periods, and the moist climate and complex geography collectively contribute to severe CWDEs. Unexpectedly, the study finds that compounding events in dry regions are mainly driven by wet extremes, whereas they show a higher dependency on dry anomalies in wet regions. The research provides new insights into compound extremes which are composed of individual hazards with distinct features. Related findings will aid decision-makers in producing effective risk mitigation plans for prioritizing vulnerable regions. Lastly, the robust framework and open access data allow for extensive exploration of various compounding hazards in different regions.

Spatial-temporal Changes and Driving Factors of Soil Microbial Communities in a Typical City of North China Plain

The structure and function of microbial communities are affected by several environmental factors. To clarify the spatial-temporal changes and main influencing factors of soil microbial communities in a typical pharmaceutical city, it is urgent to study the spatial-temporal changes in microbial communities in soils for typical cities. Shijiazhuang City was selected as the study area, and 12 sampling sites were selected. The topsoil was collected in June (summer) and September (autumn) of 2021. The 16S rRNA high-throughput sequencing technology was used to study the structure and function of microbial communities in the soil and explore their spatial-temporal changes. Concurrently, Pearson correlation analysis was applied to establish the correlation between the microbial community and environmental factors, and identify the main driving factors of temporal and spatial changes in the microbial community. The results showed that:① Actinobaciota and Proteobateria were the main dominant bacteria in the surface soil of Shijiazhuang City; at the phylum level, the relative abundance of Actinobacteria and Proteobateria decreased from summer to autumn; at the genus level, the dominant genera were Arthrobacter and unknown genera in summer and Arthrobacter and Candidatus_Nitrocosmicus in autumn, which showed significant seasonal differences (P<0.05). ② For seasonal variation, the mean values of the Simpson, Ace, and Chao indices increased, whereas the mean values of OTU decreased; for spatial variation, the Shannon and Simpson indices showed significant spatial difference (P<0.01 and P<0.05). ③ There were no significant spatial-temporal differences in various functional genes; thereinto, the relative abundances of energy production and transformation functional genes were the highest (24.06%-24.84% in summer and 24.63%-25.98% in autumn, respectively). ④ The compositions of microbial community, diversity index, and functional genes were significantly correlated with quinolone antibiotics (QNs), total phosphorus (TP), and nitrate nitrogen (NO3--N), most significantly correlated with QNs (|r|>0.900), which indicated that antibiotics were the main driving factor of soil microbial communities. Therefore, to ensure the stability of microbial community structure and function in urban soil, the comprehensive management and control of antibiotic pollution in soil should be further strengthened.

Spatial-temporal changes of compound temperature-humidity extremes in humid subtropical high-density cities: An observational study in Hong Kong from 1961 to 2020

In the humid subtropics, rising temperatures can cause higher humidity via enhanced evaporation which exacerbates heat-related health problems. This study uses multi-station observational data to reveal the spatial-temporal changes of compound temperature-humidity extreme events in Hong Kong during 1961–2020. Based on the 90th and 10th percentiles of temperature and specific humidity, four types of compound events were identified, i.e., Compound Hot and Wet (CHW), Hot and Dry (CHD), Cold and Wet (CCW), and Cold and Dry (CCD) events. Over the past six decades, there has been a significant increase of CHW (+3.45 events/decade) and decrease of CCD (−3.00 events/decade). The greatest increase of CHW was observed during the warm period of the 2010s (+4 events/year/month). Meanwhile, the trends of CHD and CCW were less evident. Spatially, more frequent compound events (especially those with high humidity (CHW and CCW)) were observed in built-up areas compared to rural areas, while the intensity of these events remained similar. The results imply that both regional climate and urban factors contribute to the increase of extreme hot and humid weather. The study generalizes mechanisms for these spatial-temporal changes, and discussed implications for compound extremes management in Hong Kong and other similar cities.

Evaluation Of The Relationship Between Spatial-Temporal Changes Of Land Use/Land Cover (Lulc) And Land Surface Temperature (Lst): A Case Study Of Nilüfer, Bursa

This study was carried out in Nilüfer district of Bursa in order to reveal the extent of urbanization, to monitor the changes in landscape elements such as water, vegetation and agricultural lands, and to examine the effects of this on Land Surface Temperature (LST). For this purpose, images taken by Sentinel-2 satellites in 2017 and 2022 were used. With these images, NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), mNDWI (Modified Normalized Difference Water Index) and NDBI (Normalized Difference Built-up Index), which are widely used in understanding terrain changes, were calculated. Time series analyzes were made between the relevant years The relationship between the changes in the field and the surface temperature was questioned by calculating the LST value with Landsat 8 OLI_TIRS images, and the relations between the indexes and the LST were evaluated by correlation analysis. The results show that NDVI, SAVI, and mNDWI are on a decreasing trend between 2017-2022, while NDBI is on an increasing trend. In other words, the results showed that the vegetation areas and water-covered surfaces decreased, while the built-up areas increased. It has been observed that the changes in Land Use/Land Cover (LULC) increase the LST in the west and south regions of the district.

Assessment of Land Surface Temperature from the Indian Cities of Ranchi and Dhanbad during COVID-19 Lockdown: Implications on the Urban Climatology

An apparent increase in average global temperature is evident globally, and India is no exception. With the recent decade (2011–2020) arguably being the warmest, significant challenges due to rapid climate change have gained attention. However, notable spatial-temporal changes, especially with regard to land surface temperature (LST), were observed during the COVID-19 pandemic lockdown period, when a comparatively cooler climate was experienced in many urban centers. Assessment of LST, crucial in many heat-balance, land use, and climate change models research studies, depicts the near-surface hotness of the Earth’s temperature at a given location. Thus, this study utilizes satellite remote-sensing data to investigate the spatial-temporal variations of LST pre and post-lockdown imposed during the COVID-19 outbreak. Unlike many existing research studies on the metropolitans of India, the study considers developing Indian cities, Ranchi and Dhanbad, as its study area. Accurate LST computation was performed using existing LANDSAT-8 OLI/TIRS images and judged using other parameters (NDVI, LSE) obtained directly from the thermal infrared bands. The LST assessment successfully estimated temperature variations in Ranchi and Dhanbad, depicting a significant drop in temperature coinciding with the lockdown period and subsequent increase in urban temperature post-pandemic, indicating a meaningful relationship between human activities and urban surface temperature.

Spatio-Temporal Changes in Forest Area and Its Ecosystem Service Value in Ganzi Prefecture, China, in the Period 1997–2017

It is possible to manage the forest ecosystem and promote sustainable development by keeping track of spatio-temporal fluctuation in the forest area and its ecosystem service value (ESV). The forest ecology of Ganzi Tibetan Autonomous Prefecture (Ganzi Prefecture), which is located in the northern Hengduan Mountains region, i.e., China’s most important ecological functional area, has seen significant alteration during the past 20 years. However, little is known about how the forest and its ESV evolve. We obtained data regarding Ganzi Prefecture’s forests using visual interpretation of remote sensing images derived from 1997, 2007, and 2017, and we evaluated the spatial–temporal changes in the forest ESV from 1997 to 2017 using global value coefficients and adjusted local value coefficients. The results revealed that (1) from 1997 to 2017, the forest area of Ganzi Prefecture increased by 6729.95 km2, and the forest growth rate was 336.50 km2/a, while (2) from 1997 to 2017, the forest ESV in Ganzi Prefecture experienced an overall increase of 257.59 × 108 yuan. The primary driver of the forest ESV increase was the implementation of forestry ecological engineering and protection policies. (3) Finally, the spatial distribution of the forest ESV revealed that the forest ESV density increased during this period, with the most significant increase occurring in Yajiang. The forest ESV was scattered with the highest density in Yajiang and the lowest density in Shiqu. This study emphasizes how crucial forest ecosystems are to Ganzi Prefecture’s mechanisms for maintaining life. It provided a scientific basis for the sustainable management of the forest ecosystem in the Hengduan Mountains.

Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability

Hydrological droughts have caused greater damage due to climate change and rapid socio-economic development. Exploring future hydrological drought variations is imperative for drought defense and infrastructure construction. This study aims to investigate future changes in hydrological droughts across the Yangtze River Basin during 2025 ∼ 2100. The Distributed Time-Variant Gain Model coupled with the Standard Operation Policy module (DTVGM-SOP) was employed to project future runoff driven by the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) data. Subsequently, future spatial–temporal changes in the characteristics of hydrological droughts were analyzed based on the Standardized Runoff Index (SRI). Lastly, future changes in the probability of concurrent hydrological droughts were evaluated using the bivariate copula functions. Results show that: (1) The DTVGM-SOP model can satisfactorily simulate monthly streamflow with a high Nash-Sutcliff Efficiency (NSE) of 0.75 ∼ 0.92. Future annual maximum discharge and annual average discharge in the mainstream and the northern tributaries are expected to increase by 3.34%∼58.19% and 3.11%∼32.66% respectively, while annual maximum discharge in the southern tributaries decreases by 0.54%∼20.09%. (2) The future annual and seasonal hydrological drought frequency is expected to decrease in most regions, while the frequency in spring increases in the Qinghai Tibet Plateau. The duration and severity of future hydrological droughts are expected to decrease by 9.52%∼78.69% and 5.13%∼81.36% in the whole basin except the source area and the Yalong River Basin. (3) Future concurrent hydrological droughts will be mitigated with decreasing frequency. The probability of concurrent hydrological droughts is expected to decrease by 46.15%∼96.47%, while future changes in the conditional probability of the mainstream are diverse in different concurrent scenarios with a range of −93.41%∼40.09%. The findings are helpful to improve the understanding of future hydrological drought variations in the Yangtze River Basin and provide essential information for damage reduction.

An integrated perspective on the spatial–temporal characteristics of China’s manufacturing carbon emissions at the regional and industry levels

Given that manufacturing industry is the second-largest source of carbon emissions in China, effective carbon emission reduction plans require a comprehensive examination of the spatial–temporal characteristics (STC) of manufacturing carbon emissions (MCE) from both regional and industrial perspectives. However, most studies on the STC of MCE focus solely on regional perspectives, overlooking the importance of integrating both regional and industrial perspectives to fully understand the STC of China’s MCE. Moreover, there is limited research on the spatial disparities in China’s MCE. Building on a detailed carbon emissions inventory of 27 manufacturing industries across 30 provinces in China from 2001 to 2020, this study employed descriptive statistics, natural breakpoint classification, spatial–temporal transition, and the Dagum Gini ratio with its cluster decomposition method to analyze the STC of MCE from regional and industrial perspectives. The assessment of STC of MCE includes an examination of both spatial–temporal changes and disparities. Results show that the MCE of the three regions in China follow the order of east > central > west, with most of the super-heavy-duty MCE and heavy-duty MCE provinces located in the eastern and central regions. China’s three-tier manufacturing industries exhibit a pattern of “low-end high-end industries with small carbon emissions, while mid-end industries have large carbon emissions”. The regional differences show that the gap in the high-end MCE among the three regions is gradually narrowing, while the gap between mid-end MCE in the east and those in the central and western regions is widening. The overall disparities of MCE exhibited a continuous widening trend. In terms of sources, inter-regional disparities had the highest contribution rate, followed by intra-regional disparities, and super-dense disparities had the least contribution. Based on the research findings, this paper proposes targeted policy suggestions for carbon emission reduction in the manufacturing industry.

Temporal–Spatial Variations and Convergence Analysis of Land Use Eco-Efficiency in the Urban Agglomerations of the Yellow River Basin in China

Achieving synergistic development of efficient urban land use and the natural environment is crucial in promoting green urbanization. The assessment of land use eco-efficiency (LUEE) and its temporal–spatial changes provides an effective means of quantifying the relationship between the urban ecological environment and land use. Targeting 55 selected cities in the Yellow River Basin (YRB), in this study, we utilize the Super-EBM method to gauge the LUEE. We explore the temporal patterns and the spatial convergence of LUEE utilizing kernel density estimation and spatial econometric methods. Considering the resource and environmental costs of land use, we assumed the industrial pollutant emissions generated during urban land use as the undesired outputs and designed a framework for measuring the level of LUEE under double constraints, which theoretically revealed the formation process and spatial convergence mechanism of LUEE. The results show the following: (1) Throughout the sample period, the LUEE of the YRB urban agglomeration decreased from 0.158 in 2009 to 0.094 in 2020, indicating a decreasing spatial disparity in LUEE over time. Notably, the Lanxi urban cluster exhibited the largest gap in LUEE, whereas the Guanzhong Plain urban agglomeration displayed the smallest gap. The hyper-variable density exceeded the inter-group gap as the main factor leading to the difference in LUEE. (2) Although the LUEE of urban agglomerations has increased, there still exists a noticeable polarization phenomenon. (3) The LUEE of YRB demonstrates a pattern of conditional convergence and exerts a significant spatial spillover effect. Over time, the LUEE of YRB will tend towards an individual steady state. The findings have implications for strengthening linkage and synergy among cities in YRB, promoting factor integration across administrative regions, and formulating heterogeneous policies.

Ecosystem carbon storage considering combined environmental and land-use changes in the future and pathways to carbon neutrality in developed regions

Assessing the carbon storage capacity of terrestrial ecosystems is crucial for land management and carbon reduction policymaking. There is still a knowledge gap regarding how ecosystem carbon storage will be impacted by combined environmental and land-use factors and their spatial-temporal changes, especially in developed regions where urbanization has slowed down. This study investigated how developed regions in subtropical and tropical areas might increase carbon storage and achieve carbon neutrality, using Guangdong Province in South China as an example. Based on the sustainable development assumption, three land-management scenarios were developed and simulated for 2020–2060 using the Patch-generating Land Use Simulation model. Without considering disturbance and natural losses, carbon storage was estimated by net ecosystem productivity (NEP)—the difference between net primary productivity (NPP) and heterotrophic respiration (HR). NPP was predicted using an artificial neural network model trained by historical NPP data and 16 environmental and land-use variables. HR was predicted using soil respiration models from previous research. Based on the balance between carbon storage and emissions, we predicted the allowable fossil fuel consumption to achieve net-zero CO2 emissions in 2060. The results show that Guangdong's total carbon storage changes from 73.7 MtC in 2020 to 70.6–74.8 MtC in 2060 under different scenarios. Nonlinear relationships exist between the carbon stored and the areas of different land-use types. Topography, temperatures, and land-use configurations jointly lead to significantly varied carbon storage between croplands and between forests in space and time. Protecting and regenerating forests in subtropical areas and forest edges is more effective than afforestation in lowland tropical areas for storing carbon. Net-zero CO2 emissions rely more on reducing emissions than land management. To achieve this, the proportion of fossil energy in total energy consumption should be lowered from 75.5 % in 2020 to ~25 % in 2060.