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Landslides are destructive natural disasters that cause human and economic losses. Although many studies report the effects of forest age on landslide susceptibility, especially for shallow landslides, no studies have examined the effects at a national scale. We assumed that temporal variations in the annual number of rainfall-triggered landslides in Japan were determined by variations in rainfall and forest age distribution. By this assumption, this study aimed to quantify the decrease in the frequency of rainfall-induced landslides owing to the increasing maturity of forests in Japan. Data were collated from 21 studies covering 11 sites in three countries that reported a landslide susceptibility index (i.e., frequency ratio or landslide density) and the relation between forest age and the normalized landslide susceptibility index (NLSI) was modeled. Using this relation and the area for each forest age class, the change in landslide susceptibility at a national scale (NLSIJpn) was quantified during 1966–2017. The authors developed generalized linear models (GLMs) using the annual number of landslides as the response variable and the NLSIJpn and a rainfall index for each year as the explanatory variables. The number of rainfall-induced landslides was simulated in the GLMs in 15 scenarios with different forest age distributions and rainfall amounts. The number of landslides in young-age-dominated and middle-age-dominated forests was estimated to be 2.4 and 1.1 times, respectively, that in mature-age-dominated forests. The change in the number of landslides from young-age-dominated to mature-age-dominated forests was larger than that from an increase in the rainfall amount of 20%. We conclude that increasing the maturity of forests greatly reduces landslide frequency in Japan. In a changing climate with potentially threatening increases in rainfall, preserving mature forests is important to avoid amplifying landslide susceptibility on a national scale. [ABSTRACT FROM AUTHOR]

The publication of Quebec's National Policy on Architecture and Regional Planning (PNAAT) challenges geography in several respects. It is the result of an 18‐month development process and is built around the sustainable development paradigm. This storytelling is part of a generalized trend in planning. In Quebec, several public policies adopted since 2000 show a strong narrative. Consequently, it is necessary to address the nature of this narrative: is it the consolidation of the existing theoretical frame or a partial or total reframing of the paradigm developed in previous policies? In addition, what potential role will be played by the storytelling resulting from the PNAAT in terms of mobilizing stakeholders? These questions will lead us to compare various Quebec public policies, in terms of terminology as well as orientations and objectives. Our results illustrate the fact that the narrative of the PNAAT is in line with the territorial policies previously proposed. In addition, the call for sustainability and the fight against climate change has an obvious mobilizing power. [ABSTRACT FROM AUTHOR]

Forests and grasslands play an important role in carbon cycling. They not only absorb CO2 from the air through vegetation biomass and soil carbon sinks, but also reduce and control the horizontal transport of soil carbon (i.e., reinforcing soil carbon storage via soil conservation), thus avoiding erosion-induced CO2 emissions. In this study, vegetation biomass and soil carbon sinks, soil carbon reinforcement and reduced carbon emissions via soil conservation by forests and grasslands were quantified on the scale of the whole of China. The analysis was based on the distribution of biomass and the soil carbon pool and soil erosion rates derived from national surveys, as well as carbon density values from field surveys and literature. In 2021, forests and grasslands in China generated 394.18 Mt C/year (y) of steady-state carbon sinks through vertical biomass and soil absorption. The biomass carbon sinks of grasslands, and those of leaves, twigs, flowers and fruits of the forests, were not taken into account when quantifying the stable biomass sink, because they can become net producers of CO2 due to seasonal withering and carryover, or they can form soil organic carbon as potential soil carbon sinks. The amount of horizontal soil carbon reinforcement in China's forests and grasslands in 2021 was 20.31 Mt C/y, which was positively correlated with the reduction in the water erosion area; consequently, vertical emissions of approximately 14.89–29.78 Mt of CO2 into the atmosphere were avoided. Overall, in 2021, China's forests and grasslands absorbed atmospheric CO2 and reduced emissions by 1.46–1.47 Gt CO2/y, equivalent to approximately 13% of China's annual fossil CO2 emissions. This study demonstrates the fact that the adoption of forest and grassland measures sequesters carbon in soil and biota and reduces the risks of CO2 emissions by both vertical and horizontal paths, which is important for achieving carbon neutrality and mitigating climate change. [ABSTRACT FROM AUTHOR]

Various stakeholders, such as modelers, policy makers, farmers, and environmental regulators need reliable soil bulk density and coarse fragment content data. These two soil parameters are necessary to calculate soil carbon and nutrients stocks, to estimate water availability for plants, or to assess soil compaction. However, measuring these two parameters is labor intensive and time consuming. Therefore, many agricultural and environmental studies often miss these two soil parameters. Here, we provide four datasets, one with bulk density and coarse fragment contents of topsoil and subsoil, measured in two campaigns of the French Soil Quality Monitoring Network (RMQS for its acronym in French), a second one with the average values for bulk density and coarse fragments of the two campaigns at 0–30 cm and 30–50 cm. The third and the fourth ones are the raw data needed to calculate the two first datasets divided by campaign. In addition, the R script for calculating the depth-weighted values per soil layer is provided

Geomorphological maps offer researchers multiple advantages when studying an area, as they can provide insights into the formation processes of the observed landforms, as well as their possible evolution in the future. The geodatabase seems to be of primary importance, as it determines the way in which the data will be organized and presented on a map, and thus the map’s functionality. There are not specific guidelines for a database for geomorphological maps, as this would be dependent, inter alia, on the scale of the maps. However, up to this day, there is not a single data base template to be used for geomorphological mapping. In each case, the cartographer/geomorphologist determines the number of fields their layers will have and their type, as well as what information they will contain, depending on the scale they are working on, the variety of landforms of their area, the time and resources available (correspondingly, the accuracy their map will have) and the purpose of its creation. Also, while several countries - not only large ones, but smaller ones as well - do have a geomorphological map at national scale, many countries, Greece among them, do not. On the contrary, only parts of them have been mapped by various researchers, who did not share any common database, which renders the combination of the individual maps difficult. But even in countries with existent national geomorphological maps, a specific and common geodatabase structure was not followed. In this way, there is no uniformity in the data and comparisons between maps, as well as combinations of data are very difficult and require a lot of work. Therefore, as we have initiated a project to create the geomorphological map of the Greek territory at 1:1,000,000 scale, we found that, prior to the beginning of the mapping, it was imperative to create a geodatabase template which we intend to follow, and which can be used as a template for geomorphological mapping at national scale by other countries (researchers and/or authorities). Thus, in this paper, we propose a database template to be used in geomorphological mapping at global scale globally, so that the data and structure of maps share common properties, thus allowing for comparisons and combinations of data where necessary. Highlights: • Proposed database structure for geomorphological mapping at national scale • Guidelines for geomorphological mapping • Basis for the geomorphological mapping of Greece at 1:1,000,000-scale

Estimations of the patterns and controls of soil organic carbon (SOC) could provide instructive insights into the potential impact of future global change on soil carbon (C). In this work, we combined GeoDetector and random forest (RF) to estimate SOC stocks in Chinese grassland ecosystems with uncertainty assessments, and identified a network of cross-correlated environmental covariates for determining SOC based on a dataset collected from 813 sampling sites (0–20 cm) collected from 2000 to 2014. We predicted that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm and that the average SOC density was 4.69 kg C/m−2(−|−). The effectiveness of using RF to predict SOC was demonstrated by an accuracy assessment based on 10-fold cross-validation, with a ratio of performance to deviation (RPD) of 2.89. The SOC stocks in southern China were lower than those in northern China. A high SOC density was found in northeastern China and on the Qinghai–Tibet Plateau. Soil properties had the strongest direct effect on SOC. Climate was significantly negatively associated with SOC and indirectly affected SOC via its effect on soil properties. Topography had a significant direct impact on vegetation, but its direct effect on SOC was relatively weak. This study emphasizes the patterns and heterogeneity of SOC stocks as well as the relative significance of climate, vegetation, soil characteristics, topography, and their complex interrelationships in controlling SOC. These results may provide a theoretical foundation for developing sustainable management systems and calibrating C process models.

Understanding and managing soil organic carbon stocks (SOCS) are integral to ensuring environmental sustainability and the health of terrestrial ecosystems. The information of soil bulk density (BD) is important in accurately determining SOCS while it is often missing in the soil database. Using 3,504 soil profiles (14,170 soil samples) that represented diverse regions across China, we investigated the effectiveness of various pedotransfer functions (PTFs), including traditional PTFs, machine learning (ML), and ensemble model (EM), in predicting BD. The results showed that refitting the parameter(s) in traditional PTFs was essential for BD prediction (coefficient of determination (R2) of 0.299–0.432, root mean squared error (RMSE) of 0.156–0.162 g cm−3, Lin’s concordance coefficient (LCCC) of 0.428–0.605). Compared to traditional PTFs, ML can greatly improve the model performance for BD prediction with R2 of 0.425–0.616, RMSE of 0.129–0.158 g cm−3 and LCCC of 0.622–0.765. Our results also showed that EM can further improve BD prediction by ensembling four ML models (R2 = 0.630, RMSE = 0.126 g cm−3, LCCC = 0.775). Using the EM model, we filled the missing BD (1207 soil profiles with 3,112 soil samples) in our database and built the SOC stock database (4,275 soil profiles with 17,282 soil samples). This study can be a good reference for gap-filling the missing BD depending on the data availability, thus contribute to a deeper understanding in soil C related climate change mitigation, ecological balance preservation and environmental sustainability promotion.

The carbon balance of the global ecosystems is significantly influenced by the soil organic carbon (SOC) pool in China's terrestrial ecosystems. Nevertheless, the current understanding of the main controlling factors of SOC in different ecosystems and their discrepancies is limited. The goal of this research was to better understand the human and environmental variables affecting SOC in China's terrestrial ecosystems. We designed a conceptual framework using 2674 samples collected from four ecosystems (grasslands, shrublands, wetlands, and croplands) in China during the 2000–2014 period, combining geodetector and multiple regression (MR) approaches to investigate the effects of environmental conditions, human activity, and their interplay on surface SOC (0–20 cm). Results showed that there were large discrepancies in the strength of influencing factors among different ecosystems. Total nitrogen (TN), mean annual temperature (MAT), and bulk density (BD) were the major factors influencing SOC in grasslands. BD, TN, and pH dominated in shrublands. For wetlands, SOC stocks were primarily attributed to maximum temperature (TMMX), MAT, and potential evapotranspiration (PET). Croplands are predominantly controlled by minimum temperature (TMMN), MAT, and TN. These results highlight that natural factors, particularly climatic and soil characteristics, were the dominant factors controlling SOC stocks in China's terrestrial ecosystems. This work also highlights that the interaction of two influencing factors, especially, pairs of soil characteristics factors, pairs of climate and soil characteristics factors, can well explain the drivers of SOC on the surface soil in China. Our study emphasizes the spatial heterogeneity of the factors that influence SOC in terrestrial ecosystems, enhancing knowledge of SOC at the national level, and providing the guideline for devising better policy to improve C sequestration and mitigate climate changes. [ABSTRACT FROM AUTHOR]

The translocation and accumulation patterns of polycyclic aromatic hydrocarbons (PAHs) in the soil-crop system have important implications for the fate of PAHs and human health. This study summarized the concentrations of 16 priority PAHs in the soils and various parts of mature winter wheat in China, sourced from a screening of previous literature in English and Chinese databases. The study analyzes the distribution characteristics, transfer patterns, and human health risks of PAHs in sites studied in Shaanxi, Henan, and Shandong provinces. The results showed that the concentrations of Σ16 PAHs in the rhizosphere soil of wheat ranged from 10.30 to 893.68 ng/g, in descending order of Shaanxi > Henan > average > Shandong. In sites with mild to moderate contamination (200 < Σ16 PAHs < 600 ng/g; i.e., Henan and Shaanxi), the concentration of Σ16 PAHs in the roots was higher than that in the stems or the grains, while in contamination-free sites (Σ16 PAHs < 200 ng/g; i.e., Shandong), the highest concentration of Σ16 PAHs was found in the stems. Generally, the concentrations of PAHs increased in the order of roots–stems–grains. The predominant PAHs in each part of wheat were 2- or 3-ring compounds, with five- or six-ring PAHs being more prevalent in wheat from Shanghe, Shandong. The bioaccumulation factors of different wheat parts from Shaanxi and Henan were consistently smaller than 1, and low- and medium-ring (2–4 rings) PAHs had bigger bioconcentration factors than high-ring (5–6 rings) PAHs. However, the accumulation of PAHs in the aboveground parts of wheat was larger than that in the underground parts of the Shandong sites. The linear regression relationship between the octanol–water partition coefficient and root concentration factor (RCF) of PAHs reflected that low and medium-ring PAHs were more easily absorbed by wheat roots than high-ring PAHs in Shaanxi and Henan. Our assessment of the health risks of oral wheat intake in adults and children by the incremental lifetime cancer risk (ILCR) model found a potential carcinogenic risk for both age groups in each province, with higher risks in adults than in children. [ABSTRACT FROM AUTHOR]

In Mongolia, overuse and degradation of groundwater is a serious issue. The authors have recently applied a process-based eco-hydrology model, NICE (National Integrated Catchment-based Eco-hydrology) to urban and mining hubs to explicitly quantify spatio-temporal variations in water availability. In this study, NICE was scaled up to the total of 29 river basins in the entire country. The model simulated the effect of past climatic change and human activity on water resources during 1980-2018 there. The model reasonably reproduced observed river discharge with a maximal value during summer rainfall seasons. The simulation also revealed heterogeneous distributions of hydrologic budget and its response to climatic and anthropogenic disturbances. In addition, the authors detected hot spots of groundwater degradation by anthropogenic activity in the national scale. Analysis of relative contribution of environmental factors further clarified the characteristics in these areas and quantified spatio-temporal trends in groundwater level due to the effects of changes in precipitation and various water uses. Generally, the result showed changes in precipitation had a large effect on changes in groundwater levels until 2000. In contrast, the model clarified human activities have recently had a large impact on groundwater level changes. This trend was particularly conspicuous in river basins with urbanization and mining development such as Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. This methodology is powerful to resolve future competition for water resources in areas with fewer inventory data that could potentially trigger conflicts between urban, mining, industry, herders and local communities. [ABSTRACT FROM AUTHOR]

Abstract Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national‐scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method. Thus, using the state‐of‐the‐art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982–2010. Compared with the nationally averaged TVCD field investigation data (27 ± 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%–85.2%, and the remaining models were underestimated by 3.7%–77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD. In conclusion, the MMRFE method can effectively improve the accuracy of national‐scale TVCD estimation, and effectively reduce the uncertainty of large‐scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components.

Rice has always been one of the major food sources for human beings, and the monitoring and planning of cultivation areas to maintain food security and achieve sustainable development is critical for this crop. Traditional manual ground survey methods have been recognized as being laborious, while remote-sensing technology can perform the accurate mapping of paddy rice due to its unique data acquisition capabilities. The recently emerged Google Earth Engine (GEE) cloud-computing platform was found to be capable of storing and computing the resources required for the rapid processing of massive quantities of remote-sensing data, thereby revolutionizing traditional analysis patterns and offering unique advantages for large-scale crop mapping. Since the phenology of paddy rice depends on local climatic conditions, and considering the vast expanse of China with its outstanding geospatial heterogeneity, a zoning strategy was proposed in this study to separate the monsoon climate zone of China into two regions based on the Qinling Mountain–Huaihe River Line (Q-H Line), while discrepant basic data and algorithms have been adopted to separately map mid-season rice nationwide. For the northern regions, optical indices have been calculated based on Sentinel-2 images, growth spectral profiles have been constructed to identify phenological periods, and rice was mapped using One-Class Support Vector Machine (OCSVM); for the southern regions, microwave sequences have been constructed based on Sentinel-1 images, and rice was mapped using Random Forest (RF). By applying this methodological system, mid-season rice at 10 m spatial resolution was mapped on the GEE for the entire Chinese monsoon region in 2021. According to the accuracy evaluation coefficients and publicly released local statistical yearbook data, the relative error of the mapped areas in each province was limited to 10%, and the overall accuracy exceeded 85%. The results could indicate that mid-season rice can be mapped more accurately and efficiently on a China-wide scale with relatively few samples based on the proposed zoning strategy and mapping methods. By adjusting the parameters, the time interval for mapping could also be further extended. The powerful cloud-computing competence of the GEE platform was used to map rice on a large spatial scale, and the results can help governments to ascertain the distribution of mid-season rice across the country in a short-term period, which would be well suited to meeting the increasingly efficient and fine-grained decision-making and management requirements. [ABSTRACT FROM AUTHOR]

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas. [ABSTRACT FROM AUTHOR]

【Background and objective】 Non-point pollution from farmland (NPF) is the main source of pollutants in surface water in China and has received attention. the progress achievements of water pollution control effects and surface water environment obvious improvements had been carried out in recent years, which involve “Three Rivers” (the Huai River, Liao River and Hai River), “Three Lakes” (the Tai Lake, Dian Lake and Chao Lake), the Songhuajiang River, three Gorges Reservoir Region, the upper and middle Yellow River, the water resource and waterline basin of South to North water diversion project. The monitoring sections of Ⅰ-Ⅲ river quality had been improved by 18%, and that of interior class Ⅴ river quality had been reduced by 15% in the last 10 years, the total surface water quality has improved significantly. As President Xi Jinping pointed out, agricultural development should not only put an end to new accounts owed by the ecological environment, but also gradually repay the old accounts, and fight a tough battle against agricultural non-point source pollution. Prevention and control of agricultural non-point source pollution is an important way to realize high quality agricultural development and water environment quality assurance. 【Objective】 The fundamental status of China’s large agricultural country is to maintain a relatively high level of fertilizer application and a higher ratio of agricultural water for a long period of time, so national food and other agricultural products security could be guaranteed. However, the problems from NPF should be paid a close attention. 【Method】 Based on the data of fertilizer application amount, emission coefficient of nitrogen and phosphorus non-point source pollution, agricultural water consumption, and the quality of farmland tail water and the influence on surface water quality were analyzed. 【Result】 The ratio of fertilizer application was decreased after 2015, in which nitrogen fertilizer and phosphorus fertilizer were reduced 13.7% and 9.1% in 2019, respectively. Agricultural water use decreased after 2012, in which the ratio of agricultural water was reduced by 2.2% in 2019. From 2007 to 2017, the emission coefficient of farmland nitrogen pollution was reduced from 5.7% to 2.4%, and the emission coefficient of farmland phosphorus pollution was reduced from 0.9% to 0.4%. The environmental quality of surface water had been improved significantly, in which the ratio of class Ⅳ+Ⅴ of river water quality was reduced by 0.8% and interior class Ⅴ was reduced by 17.4% in 2018, compared to that in 2009. NPF control has achieved a great progress and crop yields continue to increase. Agricultural tail waters all belong to interior class Ⅴ, which have a potential effect on surface water quality, especially in some local areas or parts of basin. 【Conclusion】 On a national scale, effects on surface water quality of total nitrogen and total phosphorus pollution from NPF are limited and they showed a significant decreasing trend after 2017. The keys in NPF should pay attention to some local areas and river basins, so surface water environmental quality can gradually improve.

While SoilGrids is an important source of soil property data for a wide range of environmental studies worldwide, there is currently an extreme lack of studies evaluating its accuracy against independent ground truth soil sampling data. This study aimed to provide a comprehensive insight into the accuracy of SoilGrids layers for three physical soil properties representing soil texture components (clay, silt, and sand soil contents) using ground truth data in the heterogeneous landscape of Croatia. These ground truth data consisted of 686 soil samples collected within the national project at a 0–30 cm soil depth, representing the most recent official national data available. The main specificity of this study was that SoilGrids was created based on zero soil samples in the study area, according to the ISRIC WoSIS Soil Profile Database, which is very sparse for the wider surroundings of the study area. The accuracy assessment metrics indicated an overall low accuracy of the SoilGrids data compared with the ground truth data in Croatia, with the average coefficient of determination (R2) ranging from 0.039 for silt and sand to 0.267 for clay, while the normalized root-mean-square error (NRMSE) ranged from 0.362 to 2.553. Despite the great value of SoilGrids in a vast range of environmental studies, this study proved that the accuracy of its products is highly dependent on the presence of ground truth data in the study area. [ABSTRACT FROM AUTHOR]

This article aims to advance understandings of food systems functioning at a national level and explore ways for its transformation towards sustainability and social justice. Integrating food regime theory from political economy with social metabolism from ecological economics, and surplus/reproduction from feminist economics, we develop a novel research framework which combines six dimensions—food systems governance, monetary agrifood chain, socio-metabolic agrifood chain, surplus/reproduction, socioecological impacts, and conflicts & levers of change—encompassing 34 elements linked through six key connections. The research framework highlights the role of cheap food for the social reproduction of the labouring population in capitalism. Since national states play important roles in maintaining food regimes, we conducted a critical literature review through which we identified the main contributions and limitations of studies of food regimes at the national level aimed at foreseeing exit ways beyond the current corporate food regime. This regime is one of the main drives of the overcoming of planetary boundaries. An agroecological transition and food system change is needed to address this socio-ecological crisis, and this requires new food polices at a national level as well. This is why we consider it essential to integrate social metabolism with the approaches of food regimes and surplus/reproduction. • We integrate food regimes with social metabolism and reproduction in food research. • A critical literature review on national food regimes identifies key knowledge gaps. • A research framework with six dimensions, 34 elements and six connections. • A proposal relevant for agroecology transitions towards food systems transformation. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally. [Display omitted] • The nitrogen of surface water exhibited spatiotemporal heterogeneity. • There was a probability of exceeding the unacceptable risk for children in China. • MS (37.7%) was the main nitrate source of surface water in China. • The degree of nitrate impacted by PD and land use showed diversity among basins. [ABSTRACT FROM AUTHOR]

Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national‐scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method.Thus, using the state‐of‐the‐art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982–2010.Compared with the nationally averaged TVCD field investigation data (27 ± 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%–85.2%, and the remaining models were underestimated by 3.7%–77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD.In conclusion, the MMRFE method can effectively improve the accuracy of national‐scale TVCD estimation, and effectively reduce the uncertainty of large‐scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components. [ABSTRACT FROM AUTHOR]

Improving agricultural sustainability is a global challenge, particularly for China's high-input and low-efficiency cropping systems with environmental tradeoffs. Although national strategies have been implemented to achieve Sustainable Development Goals in agriculture, the potential contributions of crop switching as a promising solution under varying future climate change are still under-explored. Here, we optimize cropping patterns spatially with the targets of enhancing agriculture production, reducing environmental burdens, and achieving sustainable fertilization across different climate scenarios. Compared with current cropping patterns, the optimal crop distributions under different climate scenarios consistently suggest allocating the planting areas of maize and rapeseed to the other crops (rice, wheat, soybean, peanut, and potato). Such crop switching can consequently increase crop production by 14.1%, with accompanying reductions in environmental impacts (8.2% for leached nitrogen and 24.0% for irrigation water use) across three representative Shared Socio-economic Pathways from 2020 to 2100. The sustainable fertilization rates vary from 148-173 kg N ha -1 in 2030 to 213-253 kg N ha -1 in 2070, significantly smaller than the current rate (305 kg N ha -1 ). These outcomes highlight large potential benefits of crop switching and fertilizer management for improving China's future agricultural sustainability., (© The Author(s) 2025. Published by Oxford University Press on behalf of National Academy of Sciences.)

Geochemical baselines are crucial to explore mineral resources and monitor environmental changes. This study presents the first Laos geochemical baseline values of 69 elements. The National-scale Geochemical Mapping Project of Lao People's Democratic Republic conducted comprehensive stream sediment sampling across Laos, yielding 2079 samples collected at 1 sample/100 km2, and 69 elements were analyzed. Based on the results of LGB value, R-mode factor analysis, and scatter plot analysis, this paper analyzes the relationship between the 69 elements and the geological background, mineralization, hypergene processes and human activities in the study area. The median values of element contents related to the average crustal values were: As, B, Br, Cs, Hf, Li, N, Pb, Sb, Zr, and SiO2, >1.3 times; Ba, Be, Cl, Co, Cr, Cu, F, Ga, Mn, Mo, Ni, S, Sc, Sr, Ti, Tl, V, Zn, Eu, Al2O3, Tot.Fe2O3, MgO, CaO, and Na2O, <0.7 times; and Ag, Au, Bi, Cd, Ge, Hg, I, In, Nb, P, Rb, Se, Sn, Ta, Th, U, W, Y, La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and K2O, 0.7–1.3 times. R-mode factor analysis based on principal component analysis and varimax rotation showed that they fall into 12 factors related to bedrock, (rare earth, ferrum-group, and major Al2O3 and K2O elements; mineralization–Au, Sb, and As) and farming activities–N, Br, S, and C). This study provides basic geochemical data for many fields, including basic geology, mineral exploration, environmental protection and agricultural production in Laos. [ABSTRACT FROM AUTHOR]

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China's ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R2 = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced. [ABSTRACT FROM AUTHOR]

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In recent years, global energy systems have experienced multiple disruptions from known threats at unprecedented severity, causing costly impacts in broader and unexpected domains. This work attempts to improve the understanding of such severity-dependent impact change for the historically unprecedented but meteorologically plausible windstorm on the present Finnish electricity grid. The wind gust speed field of the unprecedented windstorm is obtained by scaling the field of the historically most impactful windstorm upwards by 24%, a value obtained with the extreme-value-theory-based method. Windstorm impacts on the electricity supply are computed with a fragility-based electricity grid impact model, accounting for medium voltage line faults and repairs throughout the country. The lost load from 24% higher wind gust speeds increases tenfold. Impacts are limited by the significant cabling of powerlines done since 2011. The obtained spatiotemporal lost load profile provides a basis for the identification and quantification of extreme windstorm costs as well as a realistic case for broader emergency preparedness exercises. The former application is illustrated by the preliminary cost-benefit assessment for cabling in the case of an unprecedented windstorm. Finally, the reevaluation of currently used cost rates calls for an account of time dependency, critical services, and impacts on smaller economic and population segments. • 24% stronger windstorm increases the number of line faults fivefold and LL tenfold • Unprecedented LL: peak – 45% of vulnerable; after a week – 32% of historical peak • Unprecedented VoLL savings from line cabling cover at least half of its costs • New method to generate spatiotemporal value field for unprecedented windstorm • Socioeconomic bases for reevaluation of cost driving factor in a major disruption [ABSTRACT FROM AUTHOR]

The verification and monitoring of agricultural subsidy claims requires combined evaluation of several criteria at the scale of over a million cultivation units. Sentinel-2 satellite imagery is a promising data source and paying agencies are encouraged to test their pre-operational use. Here, we present the outcome of the Hungarian agricultural subsidy monitoring pilot: our goal was to propose a solution based on open-source components and evaluate the main strengths and weaknesses for Sentinel-2 in the framework of a complex set of tasks. These include the checking of the basic cultivation of grasslands and arable land and compliance to the criteria of ecological focus areas. The processing of the satellite data was conducted based on random forest for crop classification and the detection of cultivation events was conducted based on NDVI (Normalized Differential Vegetation Index) time series analysis results. The outputs of these processes were combined in a decision tree ruleset to provide the final results. We found that crop classification provided good performance (overall accuracy 88%) for 22 vegetation classes and cultivation detection was also reliable when compared to on-screen visual interpretation. The main limitation was the size of fields, which were frequently small compared to the spatial resolution of the images: more than 4% of the parcels had to be excluded, although these represent less than 3% of the cultivated area of Hungary. Based on these results, we find that operational satellite-based monitoring is feasible for Hungary, and expect further improvements from integration with Sentinel-1 due to additional temporal resolution. [ABSTRACT FROM AUTHOR]

Modelling land-use/cover (LUC) scenarios are essential issue to a better understanding of the potential future tendency in order to facilitate sustainable land management practices. Therefore, the present paper explores the simulated two LUC patterns for the year 2075, modelled through a spatially explicit model, i.e., the Conversion of Land Use and its Effects at the Small Regional Extent (CLUE-s). Hence, the location of the transitions and their quantity were analysed in comparison to the current pattern (year 2018) in order to explore the potential LUC pattern change in the 2018-2075 period. Overall, the resulting scenarios indicate an increase in built-up areas (+16%), arable lands (+3%), orchards (+13%), forests (+5%) and natural grasslands (+46%), but a decrease in vineyards (-31%), complex cultivation patterns (-21%), pastures (-9%), heterogeneous agricultural areas (-33%), scrub and/or herbaceous vegetation association (-69%), and open spaces with little or no vegetation (-43%). The analysis of the two scenarios shows that the LUC pattern does not vary significantly at national scale. However, the identified changes within the protected areas suggest that a more appropriate land management could have an important influence on the LUC system in the future. The overall scores of KSimulation (0.84) and its components, KTransition (0.97) and KTransLoc (0.86), indicate that the modelled data captured well the simulated trend in the LUC pattern, pointing to a high potential of the data to be used not only to better understand the possible impact on the LUC system, but also to explore the possible environmental and socio-economic implications. [ABSTRACT FROM AUTHOR]

[Display omitted] The rising availability of satellite-based multi-temporal interferometric datasets covering large areas of the Earth surface constitutes a huge asset in the context of operational workflows aimed at improving land risk assessment and management. In order to cost-effectively handle huge amount of data, we design a semi-automatic procedure to quickly identify, map and inventory ground and infrastructures displacements by means of spatial clustering performed over very large-scale Differential Synthetic Aperture Radar Interferometry (DInSAR) datasets. The detected deforming areas are then evaluated against the Line of Sight (LOS) velocity vector decomposition and the accessible ancillary layers for a preliminary classification of the triggering factors. We apply our methodology to the mean ascending and descending deformation maps covering the whole Italian territory resulting from 3294 and 2868 Sentinel-1 (S1) acquisitions respectively, spanning from March 2015 to December 2018 and processed through the Parallel Small BAseline Subset (P-SBAS) technique. By setting a displacement rate threshold of ± 1 cm/year, a total number of 14,638 areas resulting from both geometries are found to suffer from instability phenomena, the origin of which are in turn preliminary sorted in 11 classes split between natural causes and man-made activities. With 2 degrees of confidence, we classified landslide and subsidence events as the main causes of deformation within the Italian territory, constituting respectively 31% and 27% of the total unstable areas, followed by volcanic-related processes (22%). Lastly, we provide a complete overview of the deformation phenomena which have recently occurred on the Italian Peninsula starting from national scale statistical analysis and ending up with local scale investigations according to the deformation patterns visible through the vertical and East-West components of motion. [ABSTRACT FROM AUTHOR]

While SoilGrids is an important source of soil property data for a wide range of environmental studies worldwide, there is currently an extreme lack of studies evaluating its accuracy against independent ground truth soil sampling data. This study aimed to provide a comprehensive insight into the accuracy of SoilGrids layers for three physical soil properties representing soil texture components (clay, silt, and sand soil contents) using ground truth data in the heterogeneous landscape of Croatia. These ground truth data consisted of 686 soil samples collected within the national project at a 0–30 cm soil depth, representing the most recent official national data available. The main specificity of this study was that SoilGrids was created based on zero soil samples in the study area, according to the ISRIC WoSIS Soil Profile Database, which is very sparse for the wider surroundings of the study area. The accuracy assessment metrics indicated an overall low accuracy of the SoilGrids data compared with the ground truth data in Croatia, with the average coefficient of determination (R2) ranging from 0.039 for silt and sand to 0.267 for clay, while the normalized root-mean-square error (NRMSE) ranged from 0.362 to 2.553. Despite the great value of SoilGrids in a vast range of environmental studies, this study proved that the accuracy of its products is highly dependent on the presence of ground truth data in the study area.

Polycyclic aromatic hydrocarbons (PAHs) derived from the incomplete combustion of organic materials are associated with adverse health effects. However, little is known about PAH exposure levels and their influencing factors on a large scale in developing countries. In this study, urinary monohydroxylated metabolites of PAHs (OH-PAHs), including the metabolites of naphthalene, fluorene, phenanthrene, pyrene, chrysene, and benzo[a]pyrene, were measured in 1154 samples in the general population nationwide from 26 provincial capitals in China. Concentrations of OH-PAHs ranged from 1.39 to 228 μg/L. OH-Nap, metabolite of naphthalene, was the predominant compound, accounting for 65.1% of totals. People in eastern, southwest and northeast China, such as Shanghai, Kunming, Nanning, and Changchun, suffered more PAH exposure than other regions which might associate with sampling time, living habits of the subjects, and the imbalance of economic development and energy consumption across regions. Urinary OH-PAH concentrations were associated with body mass index, gender, and age, and smoking was the main correlating factor. Inhalation and diet might be the main exposure route of human exposure to PAHs, especially for smokers by inhalation. Hazard indices showed that no subject was exposed to PAHs with potential non-carcinogenic risk. Furthermore, the carcinogenic risk was the most significant health effects, with almost all subjects having carcinogenic risk values higher than the acceptable level of 10−6. Naphthalene and phenanthrene were the main contributors. The results also suggested a possible relationship between PAH exposure and lung cancer in the Chinese population. This first nationwide study on human internal exposure to PAHs provides a large body of scientific information for governmental decision-making about associated human health and the prevention of human exposure to PAHs.

There is a large amount of saline-alkali land in China. Through the improvement and utilization of saline-alkali land to improve the carbon content in soil, it can not only become a reserve resource of cultivated land or grazing grassland, but also become an important land “carbon sink”. In this study, we performed a comprehensive meta-analysis to identify the impact of improvement and utilization of saline-alkali soil on soil organic carbon (SOC) in China. Our results showed that the soil salt and alkali content in Heilongjiang Province and Jilin Province in China was the highest, with an SOC content between 3.05 and 17.8 g/kg and pH between 8.84 and 9.94. Among the five methods of reclamation, halophyte planting, fertilization, biochar and modifier application, only biochar and modifier application significantly increased the SOC content (p < 0.05). The content of SOC in saline-alkali soil was 2.9–6.3 g/kg before biochar application, and significantly increased to 6.2–13.05 g/kg after biochar application (p < 0.01). The SOC content was 3.05–8.12 g/kg before the application of the modifier, and significantly increased to 3.68–9 g/kg (p < 0.05) after the application of the modifier. After utilization and improvement of saline-alkali land, the total nitrogen, available phosphorus and available potassium also increased significantly (p < 0.05). This study provides a scientific basis for further understanding the improvement and utilization of saline-alkali land in China and its potential for increasing carbon sinks.

There is a critical need for a national agroecosystem model for conservation policy and environmental planning, driven by issues including harmful algal blooms, water scarcity, flooding, and other weather‐related extremes. In this study, we illustrate the feasibility of a national agroecosystem model that will downscale processes to individual fields and first‐order channels. We propose to conceptually divide the conterminous United States (U.S.) into process domains as a framework for simulating processes and management at relevant scales. Specifically, we are proposing five domains: field (1–50 ha), transition (0.2–2.0 km2), headwater (1–15 km2), tributaries (15–150 km2), and main river (>150 km2). The proposed conceptual framework hydrologically connects fields across the U.S. using the National Hydrography Dataset (NHDPlus version 2). Parameterizing the Soil and Water Assessment Tool for the national agroecosystem model resulted in 4,880,000 agricultural fields, 2,250,000 non‐agricultural hydrologic response units, and 7,130,000 transition, 1,610,000 headwater, 591,000 tributary, and 432,400 main channels. Application of this framework was shown for Hydrologic Unit Code 07120002 in central Illinois and Indiana to demonstrate the feasibility of the approach using data that is readily available across the U.S. The new connectivity framework has the potential to dramatically improve national conservation and environmental assessments performed by U.S. Department of Agriculture and U.S. Environmental Protection Agency. [ABSTRACT FROM AUTHOR]

The paper presents a methodological approach to preliminary flood risk assessment, which is conceptually based on the regional typing and integrated flood risk assessment. From the above conceptual bases, two key tasks arise: (1) establishing a basic spatial unit for preliminary flood risk assessment and (2) specifying its attributes in relation to the flood hazard and vulnerability (negative consequences), which correspond to assessment on a national scale. The basic spatial unit is defined as the municipality district. The methodological procedure of the preliminary flood risk assessment is based on an assessment of the impact of the district's attributes on the potential of the flood hazard and negative flood consequences in the form of indicators, which are aggregated to subindices and indices. The flood risk potential index is determined as an aggregation of the flood hazard potential index and flood consequences potential index. [ABSTRACT FROM AUTHOR]

In the context of accelerated development of the Silk Road Economic Belt, it is necessary to conduct in-depth research on urbanization of Central Asian countries. This paper analyzes the spatial and temporal patterns and evolution dynamics of urbanization during the period 1991–2017 from the perspective of internal-external forces. The results are as follows. (1) The urbanization process of the five Central Asian countries studied is significantly influenced by their political and economic situations and displays periodic characteristics. All five countries experienced a stagnation development stage at the beginning of independence, and then a rapid growth stage since the year 2000. The average annual growth rates of the two stages were 0.19% and 1.45%, respectively. (2) Differences in the urbanization of the studied countries are obvious, and the evolutionary characteristics of each subsystem of urbanization are different. It is therefore necessary to distinguish and clearly understand the urbanization process of each country. (3) Internal and external factors play a role in the urbanization processes of Central Asian countries. External railway transportation facilities are particularly important for the development of urbanization in these countries. The regression coefficients of railway construction length, total merchandise trade and actually utilized foreign capital are 0.5665, 0.0937 and 0.0806, respectively. (4) Countries with smaller populations and economic scales need to engage in international cooperation to promote healthy development of urbanization. The results of the study indicate that internal and external factors work together in the urbanization process of Central Asian countries, and external forces are particularly important for the development of such urbanization. [ABSTRACT FROM AUTHOR]

Aim: Intensive studies since the beginning of the 21st century have provided vast amounts of knowledge about soil microbial diversity at local and global scales. However, microbial habitats have been poorly investigated at large scale. This study aims to characterize soil bacterial habitats across France for the first time by integrating the description of numerous environmental factors and human activities. Location: We focus on the large spatial scale of mainland France using the largest spatially explicit soil sampling set available across France (2,173 soils, area = 5.5 × 105 km2). Major taxa studied: Soil bacteria and archaea were studied by a high throughput sequencing approach targeting the V3‐V4 region of the 16Sribosomal ribonucleic acid (rRNA) gene directly amplified from soil DNA. Methods: We applied decision tree learning and geostatistical approaches combining the abundant data on soil microbes and large‐scale environmental description in order to conduct a comprehensive analysis of soil bacterial and archaeal communities. Results: We identified a complex mosaic of 16 distinct terrestrial habitats, based on soil type and management (pH, C : N ratio, land use). As for plants and animals, each habitat hosted generalist and specialist taxa and a specific interaction network directly or indirectly impacted by human activities. Main conclusions: In a context of global change, our results emphasize that the policies for biodiversity and habitat conservation should now integrate soil microorganisms conceptually and technically. [ABSTRACT FROM AUTHOR]

Recently proliferating 'smart city' building efforts have lent themselves well to interpretations through the lens of the policy mobilities literature. Applying this perspective, studies have insightfully shown how policymaking centred around smart cities is at once a messy, networked process stretching across scales, while also manifesting itself in concrete practices shaped by territorial–regulatory contexts. Informed by empirical research on smart city policies in Hungary and the Netherlands, this paper argues that the policy mobilities approach tends to overemphasize the global and the local. Notwithstanding the transnational circulation of smart city ideas, the national scale continues being reproduced by these ideas as a relevant scale of urban regulation, discursive framing and strategy-making under globalization. To acknowledge this, and to move towards a more decidedly multiscalar perspective on actually existing smart urbanisms, it is suggested that we incorporate the national scale, understood as a relational set of practices and discourses, more explicitly into our analysis. Insights from the Hungarian and Dutch case studies are used to illustrate the manifold ways in which the local embedding of the globally mobile smart city concept is shaped by the national scale, as well as how the national itself is being renegotiated in this process. [ABSTRACT FROM AUTHOR]

The damage caused by surface deformation is substantial and far-reaching. Although multi-temporal interferometric synthetic aperture radar (InSAR) technology is commonly used to monitor surface deformation, it remains challenging to rapidly extract surface deformation on a national scale, especially in China, which has an area of approximately 9.6 million km2. We designed a set of robust parallel computing solutions for rapid acquisition of surface deformations throughout China. The 46,904 Sentinel-1 data covering the entire territory of China from 2018 to 2022 were processed, and a surface deformation dataset throughout China (SDDC) for this period was obtained for the first time. We used external GNSS data to evaluate the accurcy. The SDDC provided abundant deformation information that can play an important role in updating the list of geological disasters, assisting in decision-making in urban construction, and strengthening understanding of potential mechanisms. We analyzed a range of applications of this data, including the deformation of urban areas caused by the overexploitation of groundwater, facility construction, and reclamation, melting deformation of frozen soil, as well as landslide, mining, karst surface, earthquake, and reservoir dam deformation, and deformation of major transport infrastructure throughout China. Our work presents a reference for the rapid extraction of surface deformation at the national scale and provides valuable data support for scientific research and engineering applications in many fields. • A robust parallel solution for surface deformation acquisition in China was devised. • Approximetaly 47,000 images acquired by Sentinel-1 were processed. • A surface deformation dataset covering all of China for 2018–2022 was generated. • Applications of this dataset in many fields are discussed. • Results provide a reference for national-scale surface deformation acquisition. [ABSTRACT FROM AUTHOR]

[Display omitted] • A 250 m resolution black soil distribution map of China was developed. • Random forest classifier can accurately identify black soil at the national scale. • Daily mean land surface temperature and vegetation greatly affect black soil distribution. • A large area of unused black soil with reclamation potential in China. Black soil (BS) is an important soil resource. However, there is a lack of accurate information on BS spatial distribution, which affects the sustainable use and protection of BS. In this study, we linked BS distribution and machine learning. Random forest (RF) classifier and recent soil survey data were used to produce a high–precision BS distribution map in China. We analyzed environmental control factors of BS distribution. The results showed that the RF classifier method can well estimate the spatial distribution of BS at the national scale with an overall accuracy of 0.63 ∼ 0.91. The hot spots of BS distribution were mainly located in the Songnen Plain, Sanjiang Plain, the eastern side of the Greater Khingan Mountains and the western side of Changbai Mountains in Northeast China, the northeastern foot of Qilian Mountains in Qinghai–Tibet Plateau and the northern foot of Tianshan Mountains in Northwest China. Nearly 45 % BS area was covered by cropland. The daily mean land surface temperature and vegetation coverage had important controling effects on the BS distribution. Excluding the alpine mountains, BS area with potential for conversion to cropland can increase by 18.12 % (3.27 × 104 km2). The mapped BS distribution of China can serve as a benchmark for BS resource monitoring and play an important role in its assessment and protection. [ABSTRACT FROM AUTHOR]

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2 < 0.46). Climate variables such as maximum temperatures and accumulated rainfall provided additional information to the 16-day NDVI time series as they significantly improved yield predictions. Variables observed up to and around the flowering period had a particularly high predictive power with additional information gained from data during grain filling. We further showed that, while all models were sensitive to a reduction of the training set size, a large majority had not reached saturation with a data set of 125 fields (2000 pixels). This indicates that additional training data are likely to further improve the skill of the models. We estimated that observations from 75 fields (1200 pixels) are required for the best single model to reach an R2 of 0.7. We contend that machine-learning regression methods applied to climate and satellite image time series can achieve reliable crop yield monitoring across years at both the pixel and the country scale. The resulting yield estimates meet the accuracy requirements for mapping the yield gap and identifying yield gap hotspots which could be targeted for further work by agricultural researchers and advisers. [ABSTRACT FROM AUTHOR]

• EN is a spatial planning tool targeting biodiversity and ecological connectivity. • The EN identifies/maps the most valuable/sensitive areas that guarantee ecosystem functioning. • The landscape scale analysis is used to map Portuguese National EN (NEN). • Existing protected areas are insufficient to ensure landscape ecological balance. • Results accuracy allows the EN transfer to regional and municipal scales. The concept and establishment of Ecological Networks (EN) have been seen as a solution towards nature conservation strategies targeting biodiversity and ecological connectivity. Within this, the EN assumed a holistic view of land-use planning and biodiversity conservation as the core of the wider Green Infrastructure (GI) framework. The EN is considered a spatial concept recognized as a system of landscape structures or ecosystems, and a strategically connected fundamental infrastructure of abiotic and biotic systems, underlying the provision of multiple functions valuable to society. This concept moves beyond traditional approaches of "nature protection and preservation", (re)focusing on the ecosystemic approach and the "continuum naturale", emphasising the quality or potentiality of physical components, allowing the articulation with the nature conservation and at-risk areas. Portugal has long had legislation in place meant to protect the natural resources. Although the environmental policies are sectoral and unarticulated, and the environmental data is dispersed and absent. In addition, this study shows that the existing protected areas in Portugal, namely Natura 2000 and classified protected areas, are insufficient to ensure landscape ecological balance and avoid fragmentation. The main goal is to develop a methodology to map a National Ecological Network (NEN) for mainland Portugal, establish the theoretical framework of the EN/GI, by identifying and mapping the most valuable and sensitive areas that guarantee the ecosystem functioning through a multi-level ecological evaluation criteria that integrate the physical and biological systems. The Portuguese NEN map, with a 25 m spatial resolution, integrates in a single tool the Portuguese environmental policies more effectively, in order to facilitate its understanding and application into planning. Regarding the EN mapping method, it was used a GIS-based model made up of a sequence of analyses and evaluations that are driven by a GIS supported assessment of several indices/models used for each EN component. These NEN components were studied individually and collectively and the results, hierarchized in two levels, show that most of the ecological components do not overlap. The NEN1 has high biodiversity and ecological value, which means they are more vulnerable to anthropogenic activity. NEN1 covers a total of 67 % of the mainland, yet as of 2018, only 25 % is protected in nature conservation areas. Priority of action must be given to NEN1 in order to avoid/decrease landscape fragmentation, environmental risks, and natural disaster prevention. This paper contributes to the understanding of the NEN importance as an ecologically based tool towards a more sustainable landscape planning, and the basis of the development plans at national, regional and local levels in an integrated manner, instead of a compilation of disassociated often-contradictory planning tools. The benefits of a Portuguese NEN into a GI development and part of a (broader) nature base solutions by increasing the ecosystems quality and become less dependent on economic and social activities, helping in the restoration of degraded ecosystems and environmental risk prevention. Moreover, it represents the first attempt to map Portuguese EN, and addresses the lack of mapping and the inconsistent EN criteria. It is available online at http://epic-webgis-portugal.isa.ulisboa.pt. [ABSTRACT FROM AUTHOR]

Information on land use and cover changes (LUCC) is important for planning of conservation and development and thus ensure forest sustainability. The current paper assesses LUCC for the whole of the mainland Tanzania. The analyses were done using land use and cover maps covering the whole of mainland Tanzania for 1995 and 2010. For 1995, forest, bushland, grassland, cultivation and other land use and cover (built up areas, bare land, etc.) covered 43.5%, 19.8%, 23.5%, 11.2%, and 2.0% of the study area, respectively. For 2010, the same land use and cover classes covered 38.0%, 14.5%, 6.9%, 36.5%, and 4.1% of the study area, respectively. The annual rate of deforestation was 320,067 ha, which is equivalent to 0.9%. Bushland and grassland were lost at 313,745 and 969,982 ha/year, respectively. Most forest was converted to cultivation and least to other land use and cover. In conclusion, the net changes were deforestation and loss of bushland and grassland primarily due to expansion of cultivation. Further research on how to reduce or halt expansion of cultivation may shed light on improving sustainability of forest, bushland, and grassland in mainland Tanzania. [ABSTRACT FROM AUTHOR]

Abstract Economic development and urban expansion have accelerated particulate matter pollution in urban areas in China. Particulate matter-driven haze poses a serious threat to human beings from a public health point of view. Substantial evidences had linked adverse health effects with exposures to PM 2.5 , but recent research indicated that PM 10–2.5 also had great risk. However, the relative contributions of driving forces to PM 10–2.5 pollution are not well understood in the urban areas in China, and no targeted policies have been regulated to control the pollution. In this study, we quantified the contributions of potential driving factor across China with the structural equation model (SEM). Our results showed that in 2015 and 2016, the annual average concentrations of PM 10–2.5 in the 290 prefecture-level cities with a mean value of 36 and 35 μg/m3, respectively. Industrial scale contributed more to PM 10–2.5 pollution than city size and residents' activities in urban areas based on SEM results. Driving forces included in our model could explain 42% of variations in PM 10–2.5 pollution, which indicated that there existed influences from other anthropogenic sources and natural sources. Eleven targeted recommendations were then proposed to control PM 10–2.5 pollution based on our mechanism analysis. Findings from our study are beneficial to control PM 10–2.5 pollution on a national scale, and also can provide a theoretical basis for the formulation of PM 10–2.5 pollution control policy in China. Graphical abstract Unlabelled Image Highlights • PM 10–2.5 poses a serious threat to human beings from a public health point of view. • Structural equation model is used to determine the driving mechanisms. • Urban PM 10–2.5 pollution was concentrated in central regions of China. • Industrial scale contributed most to PM 10–2.5 pollution in urban areas. • Eleven recommendations to control PM 10–2.5 pollution are proposed. [ABSTRACT FROM AUTHOR]