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Temperature variation in the past periods can provide valuable insights into the characteristics and driving mechanisms of current global warming and future changes. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are commonly used to as a temperature proxy to quantitatively reconstruct past temperature changes. However, it still remains unclear whether the relation between brGDGTs and temperature is influenced by other environmental factors, thereby affecting the accuracy of quantitative reconstructions. A comprehensive analysis of the relation between brGDGTs and temperature across various climate regions can greatly enhance the accuracy of reconstructing past temperature changes. Here, we investigate the distribution patterns of soil brGDGTs using 28 new topsoils collected from altitudes ranging from 2100 to 4000 m.a.s.l. in the Hengduan Mountains in the southeastern margin of the Tibetan Plateau (TP), compiled previously published soil brGDGT data in China. Our results reveal distinct distribution patterns of soil brGDGTs between humid and arid regions. Furthermore, we further investigated the correlation between brGDGT indices and various environmental factors from both altitudinal and regional scales. At the altitudinal scale, brGDGT indices exhibit a stronger correlation with mean annual air temperature (MAAT) in alkaline soils (pH > 7) than that in acidic soils (pH < 7). At the regional scale, this correlation is higher in alkaline soils than that in acidic soils as well and is stronger in humid soils (mean annual precipitation [MAP] > 800 mm) than that in semi‐humid and semi‐arid soil (400 < MAP < 800 mm) and arid soil (MAP < 400 mm). In addition, brGDGT‐inferred temperature is more affected by MAP in humid alkaline soils but less affected in humid acidic soils. Therefore, caution is needed to use suitable brGDGT index for palaeotemperature reconstructions in various climate regions. [ABSTRACT FROM AUTHOR]

High‐quality urbanization and a healthy ecosystem are both the material basis for sustainable social development. However, the tie between terrestrial ecosystem health (TEH) and urbanization is still unclear. Therefore, we assessed the spatial and temporal dynamics of urbanization and TEH at 368 cities in China from 2000 to 2020, then explored their spatial interaction and driving mechanisms by spatial autocorrelation analysis and structural equation modeling. The results showed: (1) China's comprehensive urbanization index (UI) improved from 0.08 in 2000 to 0.10 in 2020, contributing by some national urban agglomerations such as Beijing‐Tianjin‐Hebei, Yangtze River Delta, and Pearl River Delta. (2) China's terrestrial ecosystem health index (EHI) also increased from 0.67 to 0.68. Ecosystem vigor improved significantly, while ecosystem organization and resilience both decreased. (3) EHI and UI appeared to be locally spatially dependent, and path dependence was presented at municipal scales. (4) At the national scale, urbanization positively relates to EHI, which was enhanced by social, economic, and topography factors. The dominant drivers on EHI varied among regions, and urbanization improved EHI in all regions except for the southwest. Our study demonstrated that urbanization would promote TEH by implementing high‐quality development and ecological management simultaneously, providing theoretical support for urban sustainable development and ecological management. [ABSTRACT FROM AUTHOR]

Land dissection density (LDD) provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections. A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters, soil erosion, and other related domains. Land dissection phenomena in China affects large areas with different morphological, pedological, and climatic characteristics. Prior studies have focused on the potential factors influencing the LDD at a watershed scale. However, these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale. LDD's spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained. This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China's six first-order geomorphological regions (GR1∼GR6). The results yield the following findings: (1) LDD in China ranges from 0∼4.55 km/km2, which is larger in central and eastern regions than in other regions of China; (2) dominant factors and their dominant risk subcategories vary with each geomorphological region's primary internal and external forces; (3) the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors; relief degree of land surface (RDLS) is dominant in GR1, GR2, and GR5; the slope is dominant in GR6, soil type is dominant in GR3 and GR4, and lithology plays a critical role in the dominant interactions of GR3, GR4, and GR6; (4) the interactions between factors on LDD's spatial pattern have a more significant effect than individual factors. [ABSTRACT FROM AUTHOR]

This work is based on an analysis of the spatial trends and drivers of soil C, N, and P contents and their stocks in relation to latitude, longitude, and altitude. For this purpose, the coniferous forests of Qinghai spruce (Picea crassifolia (Pc)) and Qilian juniper (Juniperus przewalskii (Jp)) within the Qilian Mountains in northwest China were selected as the objects of research. It was found that at the regional scale, the C and N contents in soil from the Pc forests were 1.34 and 1.17 folds higher than those of the Jp forests, respectively, whereas the P content was 0.92 fold lower than that of the Jp forests. Interestingly, the stock of soil C and N in the two forests was consistent, indicating an equivalent role of both in maintaining the regional C and N stocks. Secondly, the soil C and N stocks of Jp were the highest at a low altitude (< 2800 m), which indicated that the Jp forest trees retained high amounts of C and N at an altitude < 2800 m. In addition, the stocks of soil C and N correlated negatively with the latitude and positively with the longitude (P < 0.05). A partial redundancy analysis and random forests regression models showed that the stocks of soil C, N, and P were primarily controlled by the soil properties, particularly the bulk density and nutrient contents. Overall, these results provide insights into the effects of soil and terrain factors on soil C, N, and P stock, thereby providing a foundation for targeted forest conservation and rehabilitation in the Qilian Mountains in the future. [ABSTRACT FROM AUTHOR]

Grazing exclusion (GE) and rest grazing (RG) are important management systems for the restoration of degraded grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management systems on the scale dependency of species diversity, plant community indices and soil variables were determined in 32 plots in the Qilian Mountains in Gansu Province. The results show that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management systems. The species richness and species diversity indices of RG grassland were significantly higher than those of GE at the regional scale. Species richness for RG and GE plots was 128 and 98, respectively, and the Shannon–Wiener and Simpson indices for RG and GE plots were 2.26 and 2.16, and 0.85 and 0.83, respectively. Additionally, three grazing management systems had a significant effect on species richness in mountain meadows, but different management systems had significantly different effects on species diversity indices in mountain meadows and temperate steppes. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of the plant and soil variables at each scale had positive effects on species diversity. However, a negative correlation was seen between biodiversity and species coverage, mean plant height, soil porosity (SP) and bulk density (BD) under the two contrasting grazing management systems. In conclusion, choosing RG at the regional scale and selecting grazing management systems according to different grassland types at the local scale can help to restore degraded grassland vegetation. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory 2/3 (OCO-2/3) employs a full-physics inversion algorithm and estimates the column-averaged dry air mole fraction of carbon dioxide (XCO 2). This research focuses on the spatiotemporal coverage and associated uncertainties of OCO-2/3's XCO 2 measurements within the Chinese region. We undertook an extensive statistical examination of the daily, weekly, 16-day, and monthly spatiotemporal coverage of XCO 2 across China, employing data from both OCO-2 and OCO-3 and their amalgamated datasets. Additionally, we used a posterior error analysis method to rigorously evaluate the uncertainties induced by various input parameters in the XCO 2 inversion model across different regions in China. Our findings reveal that OCO-2's spatial coverage spans 2.3–53.8% in China at a spatial resolution of 1° × 1°, and OCO-3's spatial coverage spans 3.3 and 47.4% across these time scales in China. Notably, the combined coverage rates of both satellites are significantly higher, ranging from 5.4 to 72.6%. Our analysis also reveals a gradual decrease in XCO 2 coverage and inversion rates from north to south and west to east across China. A significant portion of the uncertainties in OCO-2/3 XCO 2 measurements, over 90%, were attributed to primary error sources such as instrument, aerosol, and CO 2 prior profile. Aerosol-induced errors show regional variations, with higher levels observed in Southeast China. Compared to TCCON data, the accuracy of XCO 2 measurements in the two Chinese sites was lower than the global average. Our research identifies the spatiotemporal distribution and uncertainty decomposition of carbon satellites at a regional scale in China, providing an essential reference for future research. [Display omitted] • OCO-2&3 boosted China's XCO 2 coverage by 5.4%, 29.3%, 50.8%, and 72.6% for daily, weekly, 16-day, and monthly scales. • OCO-2/3 XCO 2 coverage and efficiency decline regionally, decreasing from north to south and west to east. • Key OCO-2/3 error sources: instrument, aerosol, and CO 2 prior profile contribute to over 90% of XCO 2 errors. • Aerosol errors, 20% of XCO 2 uncertainties, are higher in Southeast China, with distinct regional variations. [ABSTRACT FROM AUTHOR]

Tire wear particles (TWP), as an emerging type of microplastics, are a significant source of contaminants in roadside soils due to their high concentration of pollutants, including polycyclic aromatic hydrocarbons (PAHs). This study explored the impact of ultraviolet (UV) exposure and natural aging on the in vitro bioaccessibility of PAHs associated with TWP in soil on a China-wide scale. Our findings suggested that UV exposure amplified the negative charge of TWP by 75 % and increased the hydrophobic groups on the particle surface. The bioaccessibility of 3- and 4-ring PAHs in TWP was significantly (p < 0.05) heightened by UV exposure. After 20 types of soils containing 2 % UV-exposed TWP underwent natural aging, the bioaccessibility of PAHs saw a significant decrease (p < 0.05) to 16-48 %, compared to 28-96 % in the unaged group. Soil pH and electrical conductivity (EC) were the two primary soil properties positively influencing the reduction of in vitro PAHs concentration and PAHs bioaccessibility. According to the prediction results, soils in southern China presented the highest potential region for the release of bioaccessible PAHs from TWP, highlighting the regional specificity of environmental impact. Our study provides valuable insights into the biological impact of PAHs associated with TWP on a regional scale, and offers scientific evidence for targeted soil risk management strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification. Google Earth Engine (GEE), a cloud computing analysis platform based on global geospatial analysis, provides a new opportunity for rapid analysis of remote sensing data. In this study, high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China. The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020, and Sentinel-2 imagery was used to verify the results. The spectral indices, texture, and terrain features of the image were derived, and their contribution to the classification accuracy of winter wheat was evaluated by scoring. Then the top nine features were selected to form an optimal feature subset. Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier, the results show that the accuracy difference between the two feature classification schemes is small, but the classification effect of all feature sets is slightly better than the optimal feature subset. The overall classification accuracy of sample plots verification was 86%-95%, the Kappa coefficient was between 0.7 and 0.85, and the percentage error of the total area was 5.42%. The research demonstrates a reliable method for mapping a wide range of winter wheat planting area, and provides a good prospect for exploring the precise mapping of other crops, which is of great significance to crop monitoring and agricultural development. [ABSTRACT FROM AUTHOR]

Hydraulic conductivity is a basic property of rocks and soil, and its estimation varies as a function of the scale and purpose of the study. This work combines geomorphological analysis, geological data, and groundwater investigations from literature to assess large-scale anisotropies in hydraulic conductivity in the North China Plain. The study area encompasses 144,000 km2 and represents the most important groundwater system of China, affected in the last 40 years by severe groundwater depletion, pollution, and subsidence. The investigation area is at the center of a massive groundwater project, the South to North Water Transfer Project, which was designed and implemented to alleviate water scarcity and facilitate environmental recovery. A conceptual model based on geomorphological evidence, supported by borehole logs, and satellite image analysis is used to assess the hydraulic conductivity anisotropies at the regional scale. The model divides the alluvial plain into four geomorphological zones with similar hydraulic conductivity characteristics. Along the general groundwater flow direction, upstream hydraulic conductivity is assumed isotropic but strong anisotropies are deduced downstream. An accurate hydraulic conductivity assessment is essential to improve quantitative studies of contaminant transport, artificial aquifer recharge, and land subsidence in the North China Plain and elsewhere. [ABSTRACT FROM AUTHOR]

Climate change and human activities have increased the risk of soil erosion, resulting in widespread soil degradation in many regions worldwide. In this study, our purpose was to estimate the spatial and temporal changes in the integrated fertility indexes (IFI) of Northeast China from the 1990s to the 2010s from a regional perspective. The soil erosion modulus, calculated using the Revised Universal Soil Loss Equation, was combined with soil nutrient data for four nutrients in two time stages, and the response of soil fertility to soil erosion was analyzed under nine land-use patterns. The results indicated that soil fertility in Northeast China had decreased by 4.46% from the 1990s to the 2010s, with the IFI of cultivated land showing the smallest decline at 1.69%, followed by grassland at 4.81%, and forest land at 6.21%, in that order. The IFI of changed land-use decreased more than that of unchanged land-use. The △IFI (IFI in the 2010s minus IFI in the 1990s) was negatively correlated with the soil erosion modulus, particularly when the average soil erosion modulus exceeded 2 t/(ha/yr). Furthermore, △IFI was mostly negative, indicating obvious soil degradation. The contribution rate of nutrient loss in the 1990s averaged over 70%, which was significantly higher than that in the 2010s, indicating that non-point source pollution control and soil erosion interception measures implemented by most city governments after 2010 had been effective. In most cases, the contribution rates of the total potassium (TK) and soil organic carbon (SOC) in the 1990s were significantly higher than those of the other indicators, with the highest contribution rate of SOC loss being observed for cultivated land and the highest contribution rate of TK loss being observed for forest land and grasslands. Forest land is conducive to maintaining soil fertility, and when other types of land are converted into forest land, the contribution rate of nutrient loss decreases. [ABSTRACT FROM AUTHOR]

Featured Application: In this paper, the innovative use of a multiscale flow-focused geographically weighted regression (MFGWR) approach to model the spatial interaction of expressway transport flow is critically evaluated. The study demonstrates varied scale effects between push and pull forces and provides datadriven evidence to inform transport planning, infrastructure construction and economic development at a regional level. Scale is a fundamental geographical concept and its role in different geographical contexts has been widely documented. The increasing availability of transport mobility data, in the form of big datasets, enables further incorporation of spatial dependencies and non-stationarity into spatial interaction modeling of transport flows. In this paper a newly developed multiscale flow-focused geographically weighted regression (MFGWR) approach has been applied, in addition to global and local Moran I indices of flow data, to model multiscale socio-economic determinants of regional transport flows between counties across the Jiangsu Province in China. The results have confirmed the power of local Moran I of flow data for identifying urban agglomerations and the effectiveness of MFGWR in exploring multiscale processes of spatial interactions. A comparison between MFGWR and flow-focused geographically weighted regression (FGWR) showed that the MFGWR approach can better interpret the heterogeneous processes of spatial interaction. [ABSTRACT FROM AUTHOR]

Although desert reclamation efforts have been used for increased crop production, their impacts on the structure of soil microbial communities remain largely understudied. Here, six adjacent pairs of different native desert and newly reclaimed cropland soils across a 3300 km transect in northern China were selected to address how microbial biomass, community composition, biogeographic pattern and the relative importance of ecological processes responded to agricultural reclamation using qPCR and high-throughput amplicon sequencing of bacterial 16S rRNA and fungal ITS genes. Agricultural intensification generally increased bacterial and fungal biomasses compared to undisturbed desert soils, while most microbial α-diversities were not significantly changed across these sampling sites. The relative abundances of some dominant microbial phyla and families significantly changed in response to agricultural intensification, which was site-dependent. Bacterial and fungal communities showed similar biogeographic patterns predominantly dominated by community turnover rather than nestedness for both desert and cropland soils, while agricultural intensification made bacterial community compositions more dissimilar. Deterministic process and stochastic process dominated bacterial and fungal community assembly in both desert and cropland soils, respectively. Agricultural intensification slightly reduced the relative importance of deterministic process for bacterial community assembly, while it conversely increased the relative importance of deterministic process to fungal community assembly by shifting the contributions of different assembly components. These results suggest that s hort-term land-use transformation from native deserts to croplands impact desert soil microbial community structure by making bacterial community more stochastic and fungal community more deterministic. [ABSTRACT FROM AUTHOR]

Gully erosion is an important soil erosion type that destroys the surface structure and causes severe soil and water loss. Currently, the quantitative relationship between the spatial distribution of gullies and lithologic-geomorphic coupling is unclear. Gullies were interpreted by using high-resolution remote sensing image data to establish a database. Correlation analysis and geographic information technology were used to quantify the correlation between the gully distribution and lithologic-geomorphic types. The results show that the gully spatial distribution is characterized by clustering, and the lithologic-geomorphic type has a large effect on the spatial distribution of gullies. The coupling zone of phyllites and schists and intermediate-relief and intermediate-altitude mountains (P-II) has the greatest clustering; the zone of loose Quaternary sediments and intermediate-altitude plains (L-IP) has the least clustering. The lithologic-geomorphic coupling affects the initiation, development, and distribution of gullies; the more fragile the lithology and the greater the relief amplitude, the easier it is for gullies to form. This study will make it possible to better understand the distribution pattern and formation mechanism of gullies at a regional scale, and it will also provide a scientific reference for regional gully management. [ABSTRACT FROM AUTHOR]

Although the patterns and drivers of soil microbial community composition are well studied, little is known about the effects of plant–soil interactions and soil depth on soil microbial distribution at a regional scale. We examined 195 soil samples from 13 sites along a climatic transect in the temperate grasslands of northern China to measure the composition of and factors influencing soil microbial communities within a 1-m soil profile. Soil microbial community composition was measured using phospholipid fatty acids (PLFA) analysis. Fungi predominated in topsoil (0–10 cm) and bacteria and actinomycetes in deep soils (40–100 cm), independent of steppe types. This variation was explained by contemporary environmental factors (including above- and below-ground plant biomass, soil physicochemical and climatic factors) >58% in the 0–40 cm of soil depth, but <45% in deep soils. Interestingly, when we considered the interactive effects between plant traits (above ground biomass and root biomass) and soil factors (pH, clay content, and soil total carbon, nitrogen, phosphorous), we observed a significant interaction effect occurring at depths of 10–20 cm soil layer, due to different internal and external factors of the plant–soil system along the soil profile. These results improve understanding of the drivers of soil microbial community composition at regional scales. [ABSTRACT FROM AUTHOR]

Perfluorooctane sulfonate (PFOS) and related substances are widely used in various industrial and commercial applications in China that ultimately discharge sufficient quantities of PFOS to the environment. It remains unclear how emissions of PFOS ultimately affect its concentrations as well as its fate in the environment. In this study, an improved Berkeley-Trent (BETR) multimedia model is developed to predict the PFOS levels with spatial and temporal distributions on unsteady state mode from 1981 to 2050, by taking the Bohai Rim of China as a case. The results showed that the modeled concentrations agreed well with the measured data. According to the model, PFOS concentrations in fresh water peaked in some months after the peak emission (2008 or 2009), whereas in urban soil the concentrations increased to peak slightly later (around 2014). Among the selected regions, Beijing and Tianjin were simulated with higher PFOS levels in the past and present because of their higher urbanization and industrialization since the 1980s, while in the future, Shandong and Liaoning are expected to have higher concentrations of PFOS than those in Beijing. The water system including coastal water, fresh water and sediment was the biggest sink for PFOS for coastal regions. Among the chemical inputs, direct primary emissions played a more important role, whereas for chemical removal processes, inter-regional advection and background outflow were the predominant pathways. The results would be useful to control the PFOS releases in China and will help the management agencies to implement the “Stockholm Convention” effectively. [ABSTRACT FROM AUTHOR]

Significant soil pH decrease has been reported in Chinese croplands in response to enhanced chemical fertilizer application and crop yields. However, the temporal and spatial variation of soil acidification rates across Chinese croplands is still unclear. We therefore assessed trends in soil acidification rates across provincial China for the period 1980–2010 by calculating inputs-outputs of major cations and anions in cropland systems. Nitrogen (N) induced proton production increased from 4.7 keq H + /ha/yr in 1980 to a peak of 11.0 keq H + /ha/yr in 1996 and remained nearly constant after 2000 at a rate of approximately 8.6 keq H + /ha/yr. The proton production induced by crop removal increased from 1.2 to 2.3 keq H + /ha/yr. The total proton production thus increased from 5.9 to 10.9 keq H + /ha/yr in the 30 years. As a result, the actual acidification rate, reflected by (base) cation losses, accelerated from 2.3 to 6.2 keq H + /ha/yr and the potential acidification rate, reflected by phosphorus accumulation accelerated from 0.2 to 1.3 keq H + /ha/yr. The national averaged total acidification rates were thus estimated to increase from 2.6 to 7.6 keq H + /ha/yr in the past 30 years. The highest soil acidification rate occurred in the Jiangsu Province with a rate of 17.9 keq H + /ha/yr, which was due to both high N application rates and high base cation removals by crops and crop residues. The combination of elevated N inputs and decreased N use efficiency (NUE) in response to those N inputs, thus enhancing the nitrate discharge, were the main reasons for the accelerated acidification in Chinese croplands. Considering the expected growth of food demand in the future, and the linkage between grain production and fertilizer N consumption, a further acceleration of soil acidification can thus be expected, unless the N inputs is reduced and/or the NUE is increased substantially. [ABSTRACT FROM AUTHOR]

Climate change and emissions rates of contaminants are expected to affect distribution and fate of Persistent Organic Pollutants (POPs) in the environment, however, studies on these combined factors are rare. In this study, Perfluorooctane Sulfonate (PFOS) is used as an example to assess how those two factors synthetically affect fate and disposition of POPs in the Bohai Rim of China by using the Berkeley-Trent-Urban-Rural (BETR-Urban-Rural) model. We set up three climate change scenarios and four emission scenarios to conduct the simulations. The results show that climate change could have significant effects on the transport and fate of PFOS mainly including advection, inter-compartmental transfer under the “worst case” emission scenario. For most grids, a remarkable decrease in concentrations of PFOS are predicted for fresh water and urban soil in the future, with precipitation and temperature being predominant factors, whilst for coastal water and rural soil, an increasing trend is predicted. Additionally, predicted sum of sources to the Bohai Sea increases greater than removals from the Bohai Sea in the future, adding evidence that concentrations of PFOS in coastal water will increase more in the future. Under scenarios of reduced emissions and climate change, concentrations of PFOS in each compartment decreased more rapidly over time. We suggest that assessment of future climate change impacts on fate of PFOS could take emission reductions into consideration. [ABSTRACT FROM AUTHOR]

Fungi play an important regulating role in terrestrial ecosystem functioning. However, their biogeographic distribution patterns along combined gradients of plant communities and environmental variables across regional spatial scales remain poorly understood. This knowledge gap is particularly pronounced in arid and semi-arid grassland ecosystems, which occupy one third of the terrestrial surface of China. Here, a regional-scale field investigation was conducted to collect soil samples from 290 plots at 52 sites along a 4000 km transect in temperate grasslands of northern China to assess the distribution patterns of fungal communities. The high-throughput Illumina sequencing revealed that soil fungal communities were dominated by Ascomycota, Basidiomycota and Zygomycota across the temperate grasslands. Fungal richness increased with plant species richness and mean annual precipitation (MAP). Bray-Cutis dissimilarities in soil fungal community composition between sites were significantly correlated with plant community composition, geographical distance, and abiotic factors including MAP, mean annual temperature, soil pH, and soil organic carbon. Variation partitioning analysis revealed that variation in fungal community composition was mainly explained by plant community composition, followed by geographical distance and abiotic factors. Structural equation models further highlighted the significant role of plant species richness in determining soil fungal richness. Taken together, these results suggest that the regional-scale distribution pattern of soil fungal communities is predominantly mediated by plant community composition, and also by abiotic factors and geographical distance in arid and semi-arid temperate grasslands. [ABSTRACT FROM AUTHOR]

During the past two decades, the exhibition industry in China has been developing rapidly and has become an important part of the modern service industry, particularly the agglomeration characteristics of exhibition enterprises highlighted on the regional scale. Although the development of theoretical research on the western exhibition industry has taken place over time, the spatial perspective has not been at the centre of attention so far. This paper aims to fill this gap and report on the agglomeration characteristics of exhibition enterprises and their influential factors. Based on data about exhibition enterprises in the Pearl River Delta (PRD) during 1991-2013, using the Ripley K function analysis and kernel density estimation, this research identifies that: 1) the exhibition enterprise on the regional scale is significantly characterized by spatial agglomeration, and the agglomeration density and scale are continuously increasing; 2) the spatial pattern of agglomeration has developed from a single-center to multi-center form. Meanwhile, this paper profiles the factors influencing the spatial agglomeration of exhibition enterprises by selecting the panel data of nine cities in the PRD in 1999, 2002, 2006 and 2013. The results show that market capacity, urban informatization level and exhibition venues significantly influence the location choice of exhibition enterprises. Among them, the market capacity is a variable that exerts a far greater impact than other factors do. [ABSTRACT FROM AUTHOR]

To identify the impact of carbon emissions in the construction industry on the society and economy at a regional scale is of great significance for the sustainable development of the construction industry. Taking Shandong, China, as an example, this study uses a process-based carbon emission assessment method to calculate the carbon emissions of the construction industry in Shandong from 2010 to 2020. Based on specific situations at different phases, ten major factors affecting the society and economy have been analyzed and identified. Then, the Seemingly Uncorrelated Regression (SUR) model was used to analyze carbon emissions and economic and social factors at each stage. The results show the following: (1) The overall carbon emissions of the construction industry in Shandong are on an increasing trend, the carbon emission intensity is on a decreasing trend, and the material production and operation stages are the main source of carbon emissions; (2) There is a significant relationship between carbon emissions in the construction phase and all influencing factors, so it should be the main phase for macro-regulation; (3) Unlike in the Ordinary Least Square (OLS) model, the error terms of the gross output value of the construction industry and the total turnover of the main building materials equations are independent; however, there is an interaction between the error terms of the remaining equations. The results provide a theoretical reference for governmental departments to set up environmental targets and make policy regulations., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed. [ABSTRACT FROM AUTHOR]

Irrigation is essential for agriculture in the middle Heihe River basin, Northwest China, due to water scarcity and dryness of climate. The diverted river water for agriculture is being gradually reduced which requires an increased water use performance to meet crop water requirements and to maintain crop yields. It is therefore crucial to better know about the present agro-hydrological processes, irrigation performance and water productivity, and to further investigate the potential water saving on a regional scale. In this study, a distributed agro-hydrological model was developed by close coupling of an agro-hydrological model (SWAP-EPIC) and ArcInfo geographic information system. Combined effects of weather, crop, soil and irrigation factors were considered. The Yingke Irrigation District (YID), in the middle Heihe River basin, was chosen as case study, where experiments were conducted at both field and regional scales in 2012–2013. Parameters relative to soils and crops were first calibrated with field observed data and the model was later used in a distributed manner to simulate the agro-hydrological process of YID. Results showed that water productivity was spatially varied and quite small due to excessive irrigation water use. Crop evapotranspiration averaged 589 mm and deep percolation was 125 mm on average, which accounted for 21% of total irrigation. Analysis of the target scenario simulation indicated that the improvement of water conveyance and irrigation scheduling could lead to a 30% reduction of deep percolation, and save 15% of irrigation water without negative effects on crop yields. [ABSTRACT FROM AUTHOR]

A precise and high-resolution spatiotemporal distribution of atmospheric carbon dioxide (CO2) is important in identifying and quantifying the CO2 source and sinks on regional scales and emissions from discrete point sources. We propose the use of a regional gap-filling method by modeling the spatiotemporal correlation structures of column-averaged CO2 dry air mole fractions (Xco2) on a regional scale, using data from the Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over mainland China. The accuracy of the gap-filling results is verified by cross-validation and comparison with ground-based measurements. As the results of the spatiotemporal gap-filling method are applied to mainland China, the correlation coefficient (r2) between the predicted values and true ones is greater than 0.85, the mean absolute prediction error is less than 1.5 ppm in cross-validation, and the seasonal cycle of the gap-filled data is generally in agreement with ground-based measurements. Finally, we compare the prediction accuracy based on our method with that based on the commonly used spatial-only kriging to further demonstrate the improved prediction accuracy. The applied regional gap-filling method, which makes full use of the multitemporal ACOS-GOSAT data, can generate a regional regular spatial distribution map of (Xco2) at high spatial and temporal resolutions. [ABSTRACT FROM PUBLISHER]

• Ecosystem service (ES) supply, social demand and coordination dynamics were assessed. • The drivers of coordination between ES supply and social demand were investigated. • The incoordination between ES supply and social demand was found to be improving. • Spatially correlations between such coordination and drivers were obtained. Accurate spatio-temporal quantifying of ecosystem services (ESs) supply and social demand and their coordination dynamics, and determining which factors affect such coordination and the spatial distribution patterns of these effects, is essential to guide targeted ecosystem management decisions. Unfortunately, quantitative data demonstrating the explicit spatial relationships between the biophysical supply and social demand for ESs, as well as identifying the spatial non-stationary response of such relationships to influencing factors, is currently inadequate. To this end, this study employed China's Loess Plateau (LP) as an example to quantify ES supply and social demand in 2000 and 2015, during which the society and ecology in this region have undergone tremendous changes. Then, the coupling coordination degree model was used to explore the coordination relationships between ES supply and social demand. Also, we analyzed the spatial correlations between such coordination relationships and various natural factors, policy factors and socio-economic factors, using geographically weighted regression. The results showed that ES supply and social demand had great spatio-temporal variations, and both the ES supply and social demand increased from 2000 to 2015. Although the incoordination between ES supply and social demand was great, such incoordination did improve from 2000 to 2015 with positive transitions from the lower coordination grades to the higher coordination grades. The occurrence of coordination between ES supply and social demand was highly correlated with vegetation cover driven by the "Grain for Green" Program, followed by slope (a natural factor) and socio-economic factors. This study helps deepen our understanding of the incoordination between the ES supply and social demand of the LP and the spatially explicit drivers of such incoordination, and could support auxiliary decision-making for regional ecosystem management. [ABSTRACT FROM AUTHOR]

Integrated food security covers three aspects: food quantity security, food quality security, and sustainable food security. Because sustainable food security requires that food security must be compatible with sustainable development, the risk assessment of sustainable food security is becoming one of the most important issues. This paper mainly focuses on the characteristics of sustainable food security problems in the major grain-producing areas in China. We establish an index system based on land resources and eco-environmental conditions and apply a dynamic assessment method based on status assessments and trend analysis models to overcome the shortcomings of the static evaluation method. Using fuzzy mathematics, the risks are categorized into four grades: negligible risk, low risk, medium risk, and high risk. A case study was conducted in one of China's major grain-producing areas: Dongting Lake area. The results predict that the status of the sustainable food security in the Dongting Lake area is unsatisfactory for the foreseeable future. The number of districts at the medium-risk range will increase from six to ten by 2015 due to increasing population pressure, a decrease in the cultivated area, and a decrease in the effective irrigation area. Therefore, appropriate policies and measures should be put forward to improve it. The results could also provide direct support for an early warning system-which could be used to monitor food security trends or nutritional status so to inform policy makers of impending food shortages-to prevent sustainable food security risk based on some classical systematic methods. This is the first research of sustainable food security in terms of risk assessment, from the perspective of resources and the environment, at the regional scale. [ABSTRACT FROM AUTHOR]

pplying allometric equations in combination with forest inventory data is an effective approach to use when qualifying forest biomass and carbon storage on a regional scale. The objectives of this study were to (1) develop general allometric tree component biomass equations and (2) investigate tree biomass allocation patterns for Pinus massoniana, a principal tree species native to southern China, by applying 197 samples across 20 site locations. The additive allometric equations utilized to compute stem, branch, needle, root, aboveground, and total tree biomass were developed by nonlinear seemingly unrelated regression. Results show that the relative proportion of stem biomass to tree biomass increased while the contribution of canopy biomass to tree biomass decreased as trees continued to grow through time. Total root biomass was a large biomass pool in itself, and its relative proportion to tree biomass exhibited a slight increase with tree growth. Although equations employing stem diameter at breast height (dbh) alone as a predictor could accurately predict stem, aboveground, root, and total tree biomass, they were poorly fitted to predict the canopy biomass component. The inclusion of the tree height ( H) variable either slightly improved or did not in any way increase model fitness. Validation results demonstrate that these equations are suitable to estimate stem, aboveground, and total tree biomass across a broad range of P. massoniana stands on a regional scale. [ABSTRACT FROM AUTHOR]

Citrus growing is regarded as an important cash crop in some regions of China. A geographic information system (GIS) was used to investigate the growing conditions of citrus orchards in Chongqing, China. Digital maps on topography, land use, soil types and climate were obtained and a digital elevation model (DEM) was produced at a scale of 1:10 000 using a GIS. A total of 50 representative orchards (2032 ha) were examined and extensive investigation were carried out in the summer of 2007. Topographic characteristics of the orchards studied were determined using the DEM. About 53% of the total area covered by the orchards has slopes of 8-25° and 4.4% has slopes steeper than 35°. The orchards were dominantly on south-facing slopes (42%). About 80% of the orchards were within 200-400 m in altitude. The orchards were mainly on soil developed on purple shale and limestone (86 and 14% of the total area, respectively). About 42% of the area has soil with a pH of less than 5.5. The majority of the study area (60%) has soil with organic matter contents of 1-2%. General guidelines for sustainable citrus production are proposed based on the topography and soil properties of the citrus orchards. The result of regional planning indicates that about one-third of the total area of Chongqing is suitable for citrus growth (2.68 × 10 ha). A GIS-based database management system provides a new perspective on the management and planning of citrus orchards in Chongqing. [ABSTRACT FROM AUTHOR]

Longjing (Dragon Well) tea from the West Lake region of Hangzhou, China, is one of the highest quality green teas prized for drinking and is often called the national drink of China. However, the ever-increasing accumulation of lead (Pb) in Westlake Longjing tea has been continually reported in the recent decades, while the exact Pb sources still remain unclear. Our purpose is to investigate the key factors that account for the concentration increase of Pb accumulated in Longjing tea based on a broad regional scale. Eighty-one young tea leaves and corresponding soil and air samples where the tea plants grow in and around, respectively, were collected from three typical Longjing tea gardens for illustrating the sources of Pb that accumulated in Longjing tea. The sampling lines were arranged with the proximity to the traffic area. Pb concentrations in the tea leaves and soil and air samples were determined, respectively. Wash procedures were also conducted to trace the removable possibility and rates of Pb in young tea leaves. Simple statistical analysis and spatial geostatistics were further applied for the investigation of the potential relationship of Pb levels among young tea leaves and the relevant soil and air samples. The regional differences and seasonal variation of Pb concentration were found for young tea leaves, with the order of Longwu > Meijiawu > Longjing and spring > summer ( p < 0.05), respectively. The significant positive correlation was found only for the Pb concentration between young tea leaves and air samples ( r = 0.34, p < 0.01). The results from wash experiment showed that a substantial portion of Pb in young tea leaves could be removed by wash, and the removable rates of Pb showed an obvious decrease with the increasing distance from the traffic area in Longwu and Meijiawu. A similar geographic distribution tendency of Pb between young tea leaves and air was also revealed by further geographic spatial analysis taking Longwu as a case. The significant correlations and similar distribution tendency of Pb concentration between young tea leaves and air suggested that atmospheric deposition could be an important, even predominant, source for Pb accumulated in Longjin tea. This non-edaphic contribution was further validated in wash experiment and testified to be distance dependent on the proximity to the main traffic area. This might be attributed to the large rough pubescent surface of tea leaves which are conducive to foliar deposition and/or absorption of Pb. The lack of correlations between young tea leaves Pb and soil Pb indicated that the contribution of soil Pb would be limited (especially near the traffic area where the influence of atmospheric Pb was dominant) and might be attributed to the low bioavailability of Pb in soils and finite uptake and transport of Pb from roots to shoots. Meanwhile, the higher Pb concentration in spring teas (compared with summer teas) might be due to the long growth period that allowed for a long time interception of Pb by young tea leaves through atmospheric deposition. The Pb that rooted in air was suggested as the main source accounting for the accumulation of Pb in Longjing Tea of the tested tea gardens. This non-edaphic contribution of atmospheric deposition was distance dependent on the proximity to the main traffic area. For alleviating the accumulation and elevation of Pb amount in Longjing Tea, efforts should be made especially during the period of spring tea growth. The additional wash procedure was also recommended before the tea leaf processing in factories. [ABSTRACT FROM AUTHOR]

Information on how large-scale forest changes affect water resources is important in China as country-wide reforestation programs are being implemented and concerns have arisen over possible water reduction. In this study, water budget analysis and statistical methods were used to assess the effects of significant forest recovery on river discharge at Guangdong Province based on 50 years of data. We used realized water yield (RWY) as a balance term between the outflows from and inflows to the province to represent the river discharge produced solely in Guangdong Province. The relationship between forest recovery and RWY was inferred after quantitatively examining other contributing variables including precipitation, potential evapotranspiration, development of impervious areas, human water consumption, and reservoir constructions. We applied time series analysis to test the statistical relationship between forest recovery and RWYs at annual, wet season, and dry season intervals. Both approaches showed that large-scale forest recovery did not cause significant water reduction over the past 50 years. This finding is contrary to the widely held perception of the trade-off relationship between carbon (reforestation) and water. There were no significant trends in precipitation or in RWY annually and in the wet season, but there was a significant increase of RWY in the dry season over the past 50 years. It is estimated that forest recovery may play a positive role in redistributing water from the wet season to the dry season and, consequently, in increasing water yield in the dry season. The implication of those research findings for future reforestation programs and water resource protection is also discussed. [ABSTRACT FROM AUTHOR]

Abstract: Statistical analysis of the heterogeneity of soil bulk density in regional scale is important to crop growth and tillage management. While most field data focus on bulk density up to a depth of 0.3 m, this study measured the bulk density of 28 in situ soil core locations up to a depth of 1.3 m. Geostatistical method was used to analyze the anisotropic spatial variability and three-dimensional Sequential Gaussian Simulation (SGSIM) was conducted. The results of nugget/sill ratio indicated that the sampling scheme was fine enough to capture the spatial correlation distance of bulk density in the vertical direction. The nugget effect, however, in the horizontal direction suggested that the sampling frequency was not enough for bulk density in that direction. The result of three-dimensional SGSIM is consistent with measured data, indicating that the simulation method is suitable for this research objective. The results may provide basic information for other agricultural management practices. [Copyright &y& Elsevier]

The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield from twelve potential related factors in Yucheng County, China. Next, robust geographically weighted regression (RGWR) was used to explore the spatially non-stationary relationships between wheat yield and its main impact factors. Then, its modeling effect was compared with that of GWR and OLSR. Last, robust geostatistical analysis was conducted for spatial soil management measures in low-yield areas. Results showed that: (i) three main impact factors on wheat yield were identified by stepwise regression, namely soil organic matter, soil total phosphorus, and pH; (ii) the spatially non-stationary effects of the main impact factors on wheat yield were revealed by RGWR but were ignored by OLSR; (iii) RGWR obtained the best modeling effect (RI = 52.31%); (iv) robust geostatistics obtains a better spatial prediction effect and the low-yield areas are mainly located in the northeast and the middle east of the study area. Therefore, the integrated yield-based methodology effectively improves soil nutrient management at a regional scale. [ABSTRACT FROM AUTHOR]

Soil organic matter decomposition rates are thermally adapted, in relation to the ambient mean annual temperature of the soil environment. This could be because of temperature sensitivity of soil extracellular enzymes involved in decomposition. The objective of this study was to determine if soil extracellular enzyme activity and its temperature sensitivity (measured as Q 10) were related to the mean annual temperature across a grassland region. We collected surface soil (0-10 cm) from 57 temperate grassland sites along a latitudinal gradient in northern China. The Q 10 of soil extracellular enzymes (β-1,4-glucosidase, β-1,4-N acetylglucosaminidase, leucine aminopeptidase and acid phosphatase) was measured at four assay temperatures (5 °C, 15 °C, 25 °C, and 35 °C). There was no relationship between the mean annual temperature and standardized extracellular enzyme activity when other influential factors (soil pH, substrates, texture) were controlled, implying no thermal adaption of extracellular enzyme activity. Soil edaphic factors like soil pH, substrates and texture explained more of the spatial variation in the standardized extracellular enzyme activity than the mean annual temperature. Although the Q 10 of all four enzymes increased significantly with mean annual temperature, the data converged at the maximum activity, and the average Q 10 values were 1.17 for β-1,4-glucosidase, 1.21 for β-1,4-N acetylglucosaminidase, 1.11 for leucine aminopeptidase and 1.08 for acid phosphatase. The positive correlation between the Q 10 of extracellular enzyme activity and mean annual temperature is not consistent with the thermodynamic theory, which predicts higher Q 10 at sites with lower mean annual temperature. This may be due to the fact that extracellular enzymes operate outside of biological cells and thus are not directly related to physiological process rates of the microbial biomass. This study contributes to our understanding of the thermal adaptation of soil extracellular enzymes, and show that grassland sites with lower mean annual temperature do not necessarily induce higher temperature sensitivity of extracellular enzymes. [Display omitted] • No thermal adaptation of soil extracellular enzyme. • Soil extracellular enzyme activity was not related to mean annual temperature. • Thermal sensitivity of extracellular enzyme activity controlled by mean annual temperature. Synopsis: Inconsistent with soil microbial respiration, no thermal adaptation was found in either soil extracellular enzymes or their temperature sensitivity. [ABSTRACT FROM AUTHOR]

Abstract: A spatially referenced biophysical model, the water and nitrogen management model (WNMM), was developed and shown to simulate dynamic soil water movement and soil–crop carbon (C) and nitrogen (N) cycling under a given agricultural management, for the purpose of identifying optimal strategies for managing water and fertiliser N under intensive cropping systems (mainly wheat–maize) in the North China Plain and other regions in the world. A uniform data structure, ARC GRID ASCII format, was used both in GIS and WNMM for achieving a close Model-GIS coupling. A significant part of WNMM adopts and modifies concepts and components from widely used models, with a focus on soil N transformations. WNMM simulates the key processes of water dynamics in the surface and subsurface of soils: including evapotranspiration, canopy interception, water movement and groundwater fluctuations; heat transfer and solute transport; crop growth; C and N cycling in the soil–crop system; and agricultural management practices (crop rotation, irrigation, fertiliser application, harvest and tillage). The model runs on a daily time step at any desired scale and is driven by lumped variables (meteorological and crop biological data) in text data format, and spatial variables (soil and agricultural management) in ARC GRID ASCII format. In particular, WNMM simulates all key N transformations in agricultural fields, including mineralisation of fresh crop residue N and soil organic N, formation of soil organic N, immobilisation in biomass, nitrification, ammonia (NH3) volatilisation, denitrification and nitrous oxide (N2O) emissions. WNMM has been successfully applied in Fengqiu County, Henan Province and Luancheng County, Hebei Province, China at site and regional scales. The key WNMM components were intensively calibrated and verified against comprehensive field measurements of soil water content, evapotranspiration, crop leaf area index and yield, NH3 volatilisation, denitrification and N2O emissions as well as nitrate (NO3 −)–N concentrations in the soil solution. A sensitivity analysis showed that WNMM was sensitive to changes in meteorological variables, soil hydraulic properties, land use and agricultural management. At the site scale, WNMM simulated well soil water content, crop growth and yield, NH3 volatilisation and soil NO3 −–N concentration. There was uncertainty in simulating soil denitrification and N2O emissions, when the predicted peaks of denitrification and N2O emissions at very wet conditions could not be confirmed because of limitations in the acetylene-inhibition method for measuring denitrification. At county scale, WNMM simulation of crop yield in Fengqiu County explained 22% of the variation in observed crop yields, and 31% of crop yield variation in Luancheng County where soil variation was less. These results are considered acceptable because factors such as soil salinity and other nutrient deficiencies, which have not been considered in this version of WNMM, may play a role. The source code for the WNMM is available on request. [Copyright &y& Elsevier]

Abstract: A process-based crop growth model is developed to predict regional crop yield, water consumption and water use efficiency (WUE) using remotely sensed data for a portion of Hebei province (88779km2), most of which is located on the North China Plain (NCP). Model inputs consist of Geographic Information System (GIS) maps of land use, Digital Elevation Model (DEM), soil texture, crop canopy leaf area index (LAI) which is retrieved from the 10-day maximum composite National Oceanic and Atmospheric Administration (NOAA)–Advanced Very High Resolution Radiometer (AVHRR) data, and daily interpolated meteorological variables. The model is run at 92367 30″×30″. resolution grids at an hourly time-step for energy balance and daily time-step for crop growth simulation. Simulated winter wheat and summer maize yields in 1992 and 1993 are compared with both the point samples and the county-level census data. For the 108 counties, the root mean square error (relative deviation) is 1124kgha-1 (23%) for winter wheat and 1359kgha-1 (33%) for summer maize, respectively. Spatial patterns of simulated crop yield and water use efficiency are strongly influenced by irrigated/rainfed conditions. The modelled grain yield for irrigated winter wheat ranges from 3900 to 7200kgha-1, which is significantly larger than when only rainfed, 100–2600kgha-1. The modelled grain yield for irrigated maize ranges from 5800 to 8600kgha-1, which is significantly larger than when only rainfed, 1400–4800kgha-1. This suggests that the potential exists to increase yield in this region, if sufficient irrigation water is supplied. However, given the over allocation of limited surface water, an increase in irrigation is unlikely, and increasing importance will be placed on maximizing regional WUE over the NCP. The water consumption (defined here as modelled evapotranspiration (ET)) for winter wheat ranges from 330 to 500 and 70 to 280mm for irrigated and rainfed conditions, respectively. The modelled ET for irrigated maize ranges from 350 to 520, and 140 to 350mm for rainfed conditions. The simulated WUE ranges from 12.25 to 15.75kgha-1mm-1 for irrigated winter wheat and from 0.5 to 8.25kgha-1mm-1 in rainfed areas. The simulated WUE ranges from 11 to 19.25kgha-1mm-1 for irrigated maize and from 5 to 11kgha-1mm-1 in rainfed areas. These, together with the results of simulated crop yields, are comparable with previous studies in the NCP, other parts of China, and internationally, indicating the potential to apply this model to other agricultural regions. This study implicates a new view for regional agricultural and water resources management by assessing regional crop yield, water consumption and WUE consistently based on modelling biophysical processes with the aid of remote sensing. [Copyright &y& Elsevier]

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. [ABSTRACT FROM AUTHOR]

As the global ecosystem has been severely disturbed by an increasing number of human activities at different scales, remote sensing technology, as an effective quantitative measure of environmental quality, has been widely used. The remote sensing ecological index (RSEI) is one of the most popular and comprehensive ecological quality assessment indices based on the remote sensing data. However, the RSEI model exhibits that the ecological environment under natural conditions is not limited by the spatial scales. In addition, the model has major shortcomings in index selection and eigenvector, which greatly limit the application of RSEI. In this paper, the RSEI model is improved and a remote sensing ecological index optimized by the regional scale (RO-RSEI) is proposed. The result of the study, conducted in Shuangyang District, Changchun City, Jilin Province, shows that the RO-RSEI model has regional ecological significance after the introduction of the scale theory of landscape ecology; the index is preferred to solve problems like the RSEI model applied mechanization and baseless index selection. Meanwhile, due to the optimization of the eigenvector contribution of the optimal index, it solves the problems like non-unique model calculation result caused by principal component analysis or even antipodal calculation result. Compared with the RSEI model, the monitoring result of RO-RSEI model can better reflect the regional ecological changes. The improved model offers the possibility of monitoring ecological environment quality with remote sensing big data and provides a scientific basis for future scholars' batch computing., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

• A growth-climate database of Pinus massoniana is established based on tree-ring data. • No regional difference in the climate sensitivity of P. massoniana growth was found. • Three key climate signals influencing P. massoniana radial growth are identified. • High summer temperatures are widespread climate stressors for P. massoniana. The effects of temperature and precipitation on tree growth of given species may vary across its distribution range. To determine how temperature and precipitation influence tree radial growth, we conducted a dendrochronology-based meta -analysis using a network of 113 tree-ring width chronologies of Pinus massoniana trees from the geographical range of the species in southern China. Our results revealed that the difference in the sensitivity of P. massoniana growth to temperature and precipitation was not observed in the entire tree species distribution and each sub-region (all P > 0.05), indicating that there are similarities with respect to the effects of temperature and precipitation on P. massoniana growth among different regions. January–March temperature (T 1–3), June–August temperature (T 6–8), and July–September precipitation (P 7–9) were the key climate signals influencing P. massoniana growth. T 1–3 and P 7–9 generally enhanced tree growth, whereas T 6–8 lead to a decrease in tree growth. Sites with significant negative correlations between tree-ring width index (RWI) and T 1–3 showed a northward shift (P < 0.05). The mean latitude of sites with negative correlations was 29.98 ± 0.77°N, whereas the mean latitudes of sites with a non-significant or positive correlation were 27.27 ± 0.28° and 27.34 ± 0.46°N, respectively. The three correlation classes (i.e., positive, negative and non-significant) between T 6–8 and RWI showed no significant shifts in longitude, latitude, and elevation (P > 0.05), indicating that high summer temperatures are widespread climate stressors for this species. The growth-climate relationship was determined by combinations of both temperature and precipitation condition of the sampling sites. Temperature seasonality, temperature annual range, and mean temperature of driest quarter of the sampling sites were major contributors to this relationship. These findings have crucial implications for predicting tree growth, introducing the selection of appropriate provenance for afforestation, as well as for climate reconstruction at the regional scale. [ABSTRACT FROM AUTHOR]

• Environmental factors drive the development of biocrusts in Mu Us Sandland. • Vegetation, aridity, slope were the top 3 factors affecting biocrust distribution. • Moss crusts distributed under low-elevation stable shrubs are disturbed easily. • Cyanobacterial crusts were high-stress resistance, as pioneers in bare land. • Restricting disturbance will promote biocrust development and its eco-function. Biological soil crusts (biocrusts) are widely distributed in arid and semiarid ecosystems and provide critical ecological functions. Understanding how biocrusts change across broad regional scales allows us to manage them more effectively under changing climates and land-use pressures. Based on field surveys, we used boosted regression trees and correlation analysis to examine the changes in cover, distribution and developmental characteristics of biocrust mosses and cyanobacteria, and environmental factors at 40 sites in the Mu Us Sandland in northwestern China. We found that higher elevation sites (~1342 m) were the most suitable for biocrust distribution, and preferred sites were characterized by greater vegetation cover (>43%), values of the aridity index (>0.34), slope (>6.6°), soil pH (>8.85) and soil organic carbon (>0.50%). Increasing levels of disturbance (>1.15 kg dung ha−1) suppressed biocrusts. Moss crust development (e.g., biomass, thickness, bulk density) was significantly positively related to vegetation cover, aridity index, and soil organic carbon, and moss crusts tended to prefer shady shrub communities at low elevations. Shady and steep (5 – 15°) slopes and higher soil nutrient contents were positively correlated with cyanobacteria development. Reduced rainfall and increasing disturbance intensity would reduce the distribution and development of biocrusts. Our study provides a basis for informed decision making about how to manage moss and cyanobacterial crusts in the Mu Us Sandland as the region becomes hotter and drier. [ABSTRACT FROM AUTHOR]

Irrigation schedule is essential for improving crop production and allocating water resources in agricultural regions that heavily rely on irrigation. This study designs a framework based on the AquaCrop model to optimize the irrigation schedule of winter wheat under dry, normal and wet hydrologic scenarios over a large region in China. The model parameters were calibrated for one cultivar using observed data from three locations in the Fenwei Plain, northern China, and were shown to slightly vary spatially across this region. Regional weather data at high spatio-temporal resolution were generated by interpolation and were combined with regional soil data on a 2 × 2 km grid to drive the model. The irrigation schedule for the study area was optimized by combining a multi-objective algorithm with the exponential efficacy coefficient method. The optimization objectives included crop yield, water use efficiency (WUE), irrigation WUE and economic irrigation benefit. The results showed that the optimized irrigation schedule performed better than the current irrigation schedule applied by the farmers under studied hydrologic scenarios, resulting in increased crop yield, WUE, irrigation WUE and irrigation economic benefit by 1.1–9.7% and decreased irrigation amount by 4.2–5.7%, depending on regions within the study area. The framework developed in this study reallocated irrigation water amounts between regions, thereby improving water allocation to achieve optimal crop yield, water use and economic benefit for the Fenwei Plain. The results can also serve as a guide for local farmers and irrigation district managers. ga1 • A framework is constructed to optimize irrigation schedule over a large region. • Regional weather data is used for hydrologic year division and driving the crop model. • Multiple objectives including crop yield and water use and economic cost are considered in the optimization. • Optimized irrigation schedule improves the performance compared to the local current schedule. [ABSTRACT FROM AUTHOR]

• Assessment of NBLs on regional scales was conducted based on the knowledges of geology, hydrogeology and anthropogenic activities. • A large drainage basin was divided into 10 GEUs according to the characteristics of hydrogeological structures and local groundwater flow systems. • Iterative 2-δ technique, Calculated distribution function and Grubbs' test were used to assess the NBLs of various GEUs. • The outcrop of Tertiary rocks, the long-term irrigation and the rapid urbanization had important influences on NBLs. Natural background level (NBL) is a prerequisite for identifying groundwater pollution and for the evaluation of pollution control effectiveness. However, due to regional differences of natural geogenic, hydrogeological, biological and atmospheric processes, together with long-term intense anthropogenic activities, it is no longer reasonable to assess NBL as a single value without including human impact. This paper shows the importance of regional division based on the knowledge of geology, hydrogeology and anthropogenic activities on the assessment of NBLs. In the study, the Guanzhong Basin, situated in central China with an area of 20,035 km2 was divided into ten groundwater environmental units (GEUs) based on hydrogeological structures and groundwater flow systems. A total of 509 shallow groundwater samples were collected throughout the basin. Based on samples from each GEU, NBLs of Na+, Ca2+, Mg2+, HCO 3 −, SO 4 2−, Cl−, F−, Cr6+, TH and TDS were evaluated by using iterative 2-δ technique (2-δ), calculated distribution function (CDF) and Grubbs' test. The results indicate the distinct characteristics of NBLs for the various GEUs: NBLs of Na+, SO 4 2−, Cl−, HCO 3 −, F− and TDS north of the Wei River obviously exceeded those in the south; NBLs of most ions for GEU-V and GEU-X were highest north and south of the Wei River, respectively. Through detailed analyses of the natural and anthropogenic influences on NBLs, we found that the outcrops of Tertiary rocks in the Li mountains determine the highest NBLs of chemical components for GEU-X south of the Wei River; long-term irrigation is responsible for the highest NBLs of TDS, Na+, Mg2+, HCO 3 −, SO 4 2− and Cl− for GEU-V; rapid urbanization in Xi'an (GEU-VIII) slows down the accumulation of chemical components (e.g. TDS) in shallow groundwater by reducing evaporation and mixing of shallow water with deep water through ground fissures. Assessment of NBLs on a regional scale lays the basis for identification of groundwater pollution and is helpful for the reasonable management and effective protection of groundwater resources. [ABSTRACT FROM AUTHOR]

• Mineral elements & SIR were applied to determine the geographical origins of maca. • Mineral elements & SIR alone discriminated the maca samples from Peru and China. • Mineral elements are better than SIR to discriminant the regions of maca in China. • K B Mn Fe Mo Cd & As are more effective to identify the origins of Chinese maca. • The applicability of these two methods in tracing maca origins differs. Maca has shown broad application prospects as a new food resource. The function of maca is directly affected by its geographical origin, and an effective method for studying traceability must be established. This study aimed to discriminate maca from different regions at large scales (Peru and China) and small scales (Yunnan, Xinjiang, and Tibet in China) through geographical authentication using the stable isotope ratio (SIR) (δ13C, δ15N, δ2H and δ18O) and mineral elemental fingerprints combined with chemometrics. For Peruvian and Chinese maca, overall discriminatory accuracy of 100% and 96.2% were obtained by applying SIR and the mineral element analysis, respectively. For maca obtained from different Chinese regions, the order of the discriminatory accuracy was mineral elements (80.2%) = SIR combined with mineral elements (80.2%) > SIR (71.9%). K, B, Mn, Fe, Mo, Cd and As were identified as the main discriminating indicators for identifying maca from different Chinese regions. [ABSTRACT FROM AUTHOR]

Understanding the spatial variation of forest productivity and its driving factors on a large regional scale can help reveal the response mechanism of tree growth to climate change, and is an important prerequisite for efficient forest management and studying regional and global carbon cycles. Pinus massoniana Lamb. is a major planted tree species in southern China, playing an important role in the development of forestry due to its high economic and ecological benefits. Here, we establish a biomass database for P. massoniana, including stems, branches, leaves, roots, aboveground organs and total tree, by collecting the published literature, to increase our understanding of net primary productivity (NPP) geographical trends for each tree component and their influencing factors across the entire geographical distribution of the species in southern China. P. massoniana NPP ranges from 1.04 to 13.13 Mg·ha−1·year−1, with a mean value of 5.65 Mg·ha−1·year−1. The NPP of both tree components (i.e., stem, branch, leaf, root, aboveground organs, and total tree) show no clear relationships with longitude and elevation, but an inverse relationship with latitude (p < 0.01). Linear mixed-effects models (LMMs) are employed to analyze the effect of environmental factors and stand characteristics on P. massoniana NPP. LMM results reveal that the NPP of different tree components have different sensitivities to environmental and stand variables. Appropriate temperature and soil nutrients (particularly soil available phosphorus) are beneficial to biomass accumulation of this species. It is worth noting that the high temperature in July and August (HTWM) is a significant climate stressor across the species geographical distribution and is not restricted to marginal populations in the low latitude area. Temperature was a key environmental factor behind the inverse latitudinal trends of P. massoniana NPP, because it showed a higher sensitivity than other factors. In the context of climate warming and nitrogen (N) deposition, the inhibition effect caused by high temperatures and the lack or imbalance of soil nutrients, particularly soil phosphorus, should be paid more attention in the future. These findings advance our understanding about the factors influencing the productivity of each P. massoniana tree component across the full geographical distribution of the species, and are therefore valuable for forecasting climate-induced variation in forest productivity. [ABSTRACT FROM AUTHOR]

• SPCA was used to analyze the environmental vulnerability factors. • The evaluation index system of regional environmental vulnerability is proposed. • Classified protection categories and proposed ecological management measures. Environmental vulnerability assessments are important for environmental management and ecological protection, especially in arid regions experiencing water-use conflicts and environmental problems. The Shiyang River Basin (SRB) has considerable ecological value for the survival and development of vast arid regions in western China. Taking the SRB, an important and typical arid inland region in China, as a case study, a framework was built to evaluate the environmental vulnerability of such regions. Twenty-three evaluation indices were selected to represent hydro-meteorological, socioeconomic, soil biological and topographical factors. A spatial principal component analysis (SPCA) was performed to determine the contribution of each index, and the environmental vulnerability distance index (EVDI) model was applied to synthesize four groups of factors. An environmental vulnerability map was classified into the risk categories none, very low, low, moderate, high and very high , which represent 6%, 5%, 8%, 16%, 26% and 38% of the total area, respectively. The results show the following: (1) The environmental vulnerability of the SRB is high, and its spatial distribution reflects interactions between human activities and natural environmental factors. (2) At the geographical unit scale, environmental vulnerability increases from south to north, which reflects obvious regularity in the vertical zonal distribution. (3) At the administrative district scale, the environmental vulnerabilities of Liangzhou and Jinchang Districts are higher than those of other cities and towns and the environmental vulnerabilities of urban areas are higher than those of rural regions. (4) Three ecological management zones are defined based on the environmental vulnerability, and they require different protective measures and management modes. The evaluation results may be helpful for arid inland regional planning, environmental restoration implementation and sustainable development improvements in the future. The assessment framework proposed and practiced can also be applied to other regions by adjusting the factors relevant to the variables of concern in the region of interest. [ABSTRACT FROM AUTHOR]

Forest fire emissions have a great impact on local air quality and the global climate. However, the current and detailed regional forest fire emissions inventories remain poorly studied. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate monthly emissions from forest fires at a spatial resolution of 500 m × 500 m in southwest China from 2013 to 2017. The spatial and seasonal variations of forest fire emissions were then analyzed at the provincial level. The results showed that the annual average emissions of CO2, CO, CH4, SO2, NH3, NOX, PM, black carbon, organic carbon, and non-methane volatile organic compounds from forest fires were 1423.19 × 103, 91.66 × 103, 4517.08, 881.07, 1545.04, 1268.28, 9838.91, 685.55, 7949.48, and 12,724.04 Mg, respectively. The forest fire emissions characteristics were consistent with the characteristics of forest fires, which show great spatial and temporal diversity. Higher pollutant emissions were concentrated in Yunnan and Tibet, with peak emissions occurring in spring and winter. Our work provides a better understanding of the spatiotemporal representation of regional forest fire emissions and basic data for forest fire management departments and related research on pollution and emissions controls. This method will also provide guidance for other areas to develop high-resolution regional forest fire emissions inventories. [ABSTRACT FROM AUTHOR]

The natural abundance of δ15N in soils is a simple tool for understanding ecosystem nitrogen cycling. However, the main drivers for soil δ15N in different natural land use types are not clear enough. To investigate the main drivers for soil δ15N in different land uses, we measured δ15N in the top 10 cm of the soil along a precipitation gradient with various land uses on the Loess Plateau of China, which covers a total area of 620,000 km2 and is one of the most erodible area in the world, to determine effects of climatic, soil and vegetation factors on soil δ15N and the relationships between them using the partial least squares regression (PLSR) method. Soil δ15N ranged from 1‰ to 9‰ on the Loess Plateau with an average of 5.8‰ in forest lands, 5.6‰ in shrub lands, and 4.8‰ in grasslands. Calculated variable influence on projection by PLSR showed that both soil properties (including soil organic carbon content, total nitrogen content and soil texture) and mean annual precipitation influenced soil δ15N strongly. In addition, site elevation also influenced soil δ15N strongly in forest lands. The dominant controls of soil δ15N varied among three land use types on the Chinese Loess Plateau, and PLSR would be a reasonable tool to predict soil δ15N. • Soil δ15N was analyzed on the Loess Plateau and globally in different land uses. • Precipitation, soil C and N influenced soil δ15N strongly on the Loess Plateau. • Equations between soil δ15N and the main factors were built on the Loess Plateau. • Regional equations did not work on global scale due to large precipitation range. [ABSTRACT FROM AUTHOR]

Nano and colloidal particles (1-1000 nm) play important roles in phosphorus (P) migration and loss from agricultural soils; however, little is known about their relative distribution in arable crop soils under varying agricultural geolandscapes at the regional scale. Surface soils (0-20 cm depth) were collected from 15 agricultural fields, including two sites with different carbon input strategies, in Zhejiang Province, China, and water-dispersible nanocolloids (0.6-25 nm), fine colloids (25-160 nm), and medium colloids (160-500 nm) were separated and analyzed using the asymmetrical flow field flow fractionation technique. Three levels of fine-colloidal P content (3583-6142, 859-2612, and 514-653 μg kg -1 ) were identified at the regional scale. The nanocolloidal fraction correlated with organic carbon (C org ) and calcium (Ca), and the fine colloidal fraction with C org , silicon (Si), aluminum (Al), and iron (Fe). Significant linear relationships existed between colloidal P and C org , Si, Al, Fe, and Ca and for nanocolloidal P with Ca. The organic carbon controlled colloidal P saturation, which in turn affected the P carrier ability of colloids. Field-scale organic carbon inputs did not change the overall morphological trends in size fractions of water-dispersible colloids. However, they significantly affected the peak concentration in each of the nano-, fine-, and medium-colloidal P fractions. Application of chemical fertilizer with carbon-based solid manure and/or modified biochar reduced the soil nano-, fine-, and medium-colloidal P content by 30-40%; however,the application of chemical fertilizer with biogas slurry boosted colloidal P formation. This study provides a deep and novel understanding of the forms and composition of colloidal P in agricultural soils and highlights their spatial regulation by soil characteristics and carbon inputs.

Background: Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM)., Results: Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively., Conclusion: It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution; (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95; (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

The quantitative study of urban-rural gradients for persistent organic pollutants (POPs) is extremely important to understand the behavior of POPs as well as for ecological risk assessment and management. In this study, a practical urban-rural gradient model (URGM) was developed using atmospheric point source diffusion combined with a fugacity approach to test potential mathematical relationships among urban and rural soils. The mean value of polycyclic aromatic hydrocarbons (PAHs) for urban soils (0-2-km sites) was 570.80 ng/g, and was approximately 3.5 times higher than rural soils (30-50 km sites). Significant linear correlations were found between the amounts of PAHs in the surface soil and the city population and between the soil concentration and artificial surface area. Urban-rural PAH concentrations were simulated by the URGM and calibrated by city population and land-cover data, with average relative errors of 12.84%. The results showed that the URGM was suitable for simulating urban-rural PAH concentrations at a regional scale. The combustion of fossil fuels, biomass, and coal was the main source of soil PAHs in the study area, and the characteristic ratios of PAHs indicated a transition trend from pyrogenic to petrogenic sources along the urban-rural transects. This study thus provides a combined method for quantifying urban-rural gradients of PAHs and can thereby promote quantitative research on coupling among land cover, socio-economic data, and POP concentrations., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The potential toxic risk of mercury (Hg) and arsenic (As) in the soils of mining regions and other artificially disturbed lands receives considerable research attention. However, limited investigation has been conducted into the surface soils of natural globally distributed ecosystems, for example peatlands. In this study, we examine the distribution, controlling factors, sources, and potential ecological risks of Hg and As in 96 samples from 42 peatlands in the Changbai Mountains of northeastern China. The results showed that average concentrations (dry weight) of Hg and As at the samples sites were 169.1 ± 0.1 µg kg -1  and 13.0 ± 7.7 mg kg -1 , respectively. The distribution of Hg is largely determined by latitude and altitude, while As is controlled more by pH, total organic carbon (TOC), and ratio of TOC and nitrogen (C/N) at the regional scale. Variations in TOC, C/N ratio, and redox conditions contribute to determining the distribution of Hg, while TOC and redox conditions mainly affected the distribution of Arsenic at the local scale. Mercury mostly comes from regional atmospheric wet deposition, whereas elevated concentrations of As are related to local anthropogenic activities. Overall, Hg and As in the peatlands of the Changbai Mountains pose a moderate level of potential risk to ecological health.