Controls on the size distributions of shallow landslides.

Rainfall-triggered shallow landslides are destructive hazards and play an important role in landscape processes. A theory explaining the size distributions of such features remains elusive. Prior work connects size distributions to topography, but field-mapped inventories reveal pronounced similarities in the form, mode, and spread of distributions from diverse landscapes. We analyze nearly identical distributions occurring in the Oregon Coast Range and the English Lake District, two regions of strikingly different topography, lithology, and vegetation. Similarity in minimum sizes at these sites is partly explained by theory that accounts for the interplay of mechanical soil strength controls resisting failure. Maximum sizes, however, are not explained by current theory. We develop a generalized framework to account for the entire size distribution by unifying a mechanistic slope stability model with a flexible spatial-statistical description for the variability of hillslope strength. Using hillslope-scale numerical experiments, we find that landslides can occur not only in individual low strength areas but also across multiple smaller patches that coalesce. We show that reproducing observed size distributions requires spatial strength variations to be strongly localized, of large amplitude, and a consequence of multiple interacting factors. Such constraints can act together with the mechanical determinants of landslide initiation to produce size distributions of broadly similar character in widely different landscapes, as found in our examples. We propose that size distributions reflect the systematic scale dependence of the spatially averaged strength. Our results highlight the critical need to constrain the form, amplitude, and wavelength of spatial variability in material strength properties of hillslopes.

Spatial variability in herbaceous plant phenology is mostly explained by variability in temperature but also by photoperiod and functional traits.

Whereas temporal variability of plant phenology in response to climate change has already been well studied, the spatial variability of phenology is not well understood. Given that phenological shifts may affect biotic interactions, there is a need to investigate how the variability in environmental factors relates to the spatial variability in herbaceous species' phenology by at the same time considering their functional traits to predict their general and species-specific responses to future climate change. In this project, we analysed phenology records of 148 herbaceous species, which were observed for a single year by the PhenObs network in 15 botanical gardens. For each species, we characterised the spatial variability in six different phenological stages across gardens. We used boosted regression trees to link these variabilities in phenology to the variability in environmental parameters (temperature, latitude and local habitat conditions) as well as species traits (seed mass, vegetative height, specific leaf area and temporal niche) hypothesised to be related to phenology variability. We found that spatial variability in the phenology of herbaceous species was mainly driven by the variability in temperature but also photoperiod was an important driving factor for some phenological stages. In addition, we found that early-flowering and less competitive species characterised by small specific leaf area and vegetative height were more variable in their phenology. Our findings contribute to the field of phenology by showing that besides temperature, photoperiod and functional traits are important to be included when spatial variability of herbaceous species is investigated.

Cross-scale spatial variability and associations of carbon pools provide insight into regulating carbon sequestration in tropical montane rainforests.

The spatial distribution of plant, soil, and microbial carbon pools, along with their intricate interactions, presents a great challenge for the current carbon cycle research. However, it is not clear what are the characteristics of the spatial variability of these carbon pools, particularly their cross-scale relationships. We investigated the cross-scale spatial variability of microbial necromass carbon (MNC), soil organic carbon (SOC) and plant biomass (PB), as well as their correlation in a tropical montane rainforest using multifractal analysis. The results showed multifractal spatial variations of MNC, SOC, and PB, demonstrating their adherence to power-law scaling. MNC, especially low MNC, exhibited stronger spatial heterogeneity and weaker evenness compared with SOC and PB. The cross-scale correlation between MNC and SOC was stronger than their correlations at the measurement scale. Furthermore, the cross-scale spatial variability of MNC and SOC exhibited stronger and more stable correlations than those with PB. Additionally, this research suggests that when SOC and PB are both low, it is advisable for reforestations to potentiate MNC formation, whereas when both SOC and PB are high some thinning can be advisable to favour MNC formation. Thus, these results support the utilization of management measures such as reforestation or thinning as nature-based solutions to regulate carbon sequestration capacity of tropical forests by affecting the correlations among various carbon pools.

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India.

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network.

BACKGROUND: Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. PURPOSE: To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). STUDY TYPE: Prospective. POPULATION: A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. FIELD STRENGTH/SEQUENCE: A 3.0 T/resting-state functional MRI using the gradient echo sequence. ASSESSMENT: A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. STATISTICAL TESTS: Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. RESULTS: The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD. DATA CONCLUSION: Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope.

To ensure sustainable agricultural management, there is a need not only to quantify soil erosion rates but also to obtain information on the status of soil water content and soil loss under different soil types and land uses. A clear understanding of the temporal dynamics and the soil moisture spatial variability (SMSV) will help to control soil degradation by hydrological processes. This study represents the first attempt connecting cosmic-ray neutron sensors (CRNS) with soil erosion research, a novel approach to explore the complex relationships between soil water content (SWC) and soil redistribution processes using two of the most powerful nuclear techniques, CRNS and fallout 137Cs. Our preliminary results indicate that CRNS captured soil moisture dynamics along the study toposequence and demonstrated the sensitivity of neutron sensors to investigate the effect of parent material on soil water content. The Empirical Orthogonal Function (EOF) analysis of the comprehensive data from seven CRNS surveys revealed that one dominant spatial structure (EOF1) explains 89.2% of SMSV. The soil redistribution rates estimated with 137Cs at the nine locations along the hillslope, together with local factors related to soil properties (SOC, soil depth, hydraulic conductivity) and land use showed significant correlations with EOF. This study provides strong field evidence that soil type significantly affect SMSV, highlighting the key impact on soil erosion and sedimentation rates. Nevertheless, more research is needed to investigate the specific contributions of soil properties to the spatial variability of soil moisture and their subsequent effects on soil redistribution dynamics of interest for soil management.

A Smart Crop Water Stress Index-Based IoT Solution for Precision Irrigation of Wine Grape.

The Scholander-type pressure chamber to measure midday stem water potential (MSWP) has been widely used to schedule irrigation in commercial vineyards. However, the limited number of sites that can be evaluated using the pressure chamber makes it difficult to evaluate the spatial variability of vineyard water status. As an alternative, several authors have suggested using the crop water stress index (CWSI) based on low-cost thermal infrared (TIR) sensors to estimate the MSWP. Therefore, this study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs in a drip-irrigated Cabernet Sauvignon vineyard under two levels of water stress. For this study, the MLX90614 sensor was used to measure canopy temperature (Tc), and thus compute the CWSI. The results indicated that good performance of the MLX90614 infrared thermometers was observed under laboratory and vineyard conditions with root mean square error (RMSE) and mean absolute error (MAE) values being less than 1.0 °C. Finally, a good nonlinear correlation between the MSWP and CWSI (R2 = 0.72) was observed, allowing the development of intra-vineyard spatial variability maps of MSWP using the low-cost wireless infrared sensor network.

Spatial variability and source analysis of typical soil trace elements at permafrost section along national highway 214 in the eastern Qinghai-Tibet Plateau.

This paper attempts to reveal the enrichment status, spatial characteristics and material sources of typical soil trace elements at permafrost section along National Highway 214 on the Qinghai-Tibet Plateau. Therefore, the samples of typical trace elements in surface soil, being located at the northern slope of Bayan Kara Mountains, were collected and tested. The concentrations of typical trace elements in soil were analysed by mathematical statistics, spatial analysis and ecological assessment. The results show that: (1) the concentrations of As, Cd and Hg in the soil are higher than the local background values, and their degrees of variation were high. There was a certain degree of accumulation. Soil As and Hg elements constitute "slight pollution", indicating there is a none-to-slight ecological hazard. (2) The distributions of soil As, Cd, Pb and Zn concentrations are lower near the highway and increase with distance from it and then become relatively low further away. The distributions of Cr, Cu, Hg and Ni concentrations show no obvious trends in any direction. (3) The spatial heterogeneity of typical trace elements in soil is affected by soil organic matter (SOM), cation exchange capacity (CEC), pH, slope curvature and aspect. At the local scale, soil texture and topography were the main affecting factors. Concentrations of Cd, Cr, Cu, Ni, Pb and Zn were mainly affected by natural factors, while those of As and Hg were affected by both natural and human factors.

Predators mitigate the destabilising effects of heatwaves on multitrophic stream communities.

Amidst the global extinction crisis, climate change will expose ecosystems to more frequent and intense extreme climatic events, such as heatwaves. Yet, whether predator species loss-a prevailing characteristic of the extinction crisis-will exacerbate the ecological consequences of extreme climatic events remains largely unknown. Here, we show that the loss of predator species can interact with heatwaves to moderate the compositional stability of ecosystems. We exposed multitrophic stream communities, with and without a dominant predator species, to realistic current and future heatwaves and found that heatwaves destabilised algal communities by homogenising them in space. However, this happened only when the predator was absent. Additional heatwave impacts on multiple aspects of stream communities, including changes to the structure of algal and macroinvertebrate communities, as well as total algal biomass and its temporal variability, were not apparent during heatwaves and emerged only after the heatwaves had passed. Taken together, our results suggest that the ecological consequences of heatwaves can amplify over time as their impacts propagate through biological interaction networks, but the presence of predators can help to buffer such impacts. These findings underscore the importance of conserving trophic structure, and highlight the potential for species extinctions to amplify the effects of climate change and extreme events.

Polycyclic Aromatic Hydrocarbons in the Marine Atmosphere from the Western Pacific to the Southern Ocean: Spatial Variability, Gas/Particle Partitioning, and Source Apportionment.

The spatial variability of polycyclic aromatic hydrocarbons (PAHs) in the marine atmosphere contributes to the understanding of the global sources, fate, and impact of this contaminant. Few studies conducted to measure PAHs in the oceanic atmosphere have covered a large scale, especially in the Southern Ocean. In this study, high-volume air samples were taken along a cross-section from China to Antarctica and analyzed for gaseous and particulate PAHs. The data revealed the spatial distribution, gas-particle partitioning, and source contributions of PAHs in the Pacific, Indian, and Southern Oceans. The median concentration (gaseous + particulate) of ∑24PAHs was 3900 pg/m3 in the Pacific Ocean, 2000 pg/m3 in the Indian Ocean, and 1200 pg/m3 in the Southern Ocean. A clear latitudinal gradient was observed for airborne PAHs from the western Pacific to the Southern Ocean. Back trajectories (BTs) analysis showed that air masses predominantly originated from populated land had significantly higher concentrations of PAHs than those from the oceans or Antarctic continents/islands. The air mass origins and temperature have significant influences on the gas-particle partitioning of PAHs. Source analysis by positive matrix factorization (PMF) showed that the highest contribution to PAHs was from coal combustion emissions (52%), followed by engine combustion emissions (27%) and wood combustion emissions (21%). A higher contribution of PAHs from wood combustion was found in the eastern coastal region of Australia. In contrast, engine combustion emissions primarily influenced the sites in Southeast Asia.

Microclimatic Evaluation of Five Types of Colombian Greenhouses Using Geostatistical Techniques.

In Colombia, the second-largest exporter of cut flowers worldwide and one of the South American countries with the largest area of crops under cover, passive or naturally ventilated greenhouses predominate. Locally, there are several types of greenhouses that differ in architecture, size, height, shape of roof and ventilation surfaces, of which many characteristics of the microclimate generated in their interior environment are unknown. This generates productive limitations that in some way may be limiting the yield, quality and health of the final products harvested; in addition, Colombian producers do not have the ability to monitor the microclimate of their farms, much less to correlate microclimate data with data on crop production and yield. Therefore, there is a need for the Colombian grower to know the most relevant microclimate characteristics generated in the main greenhouses used locally. The objective of this work was to carry out a microclimatic characterization of the five most used types of greenhouses in Colombia. The main results allowed determining that in these structures, there are conditions of high humidity and low vapor pressure for several hours of the day, which affects the physiological processes of growth and development of the plants. It was also identified that for each type of greenhouse, depending on the level of radiation, there is a significant microclimatic heterogeneity that may be the cause of the heterogeneity in plant growth, which is a common characteristic observed by the technical cultivation personnel. Therefore, it can be concluded that it is urgent to propose microclimatic optimization strategies to help ensure the sustainability of the most important production systems in the country.

Progress in research on site-specific nutrient management for smallholder farmers in sub-Saharan Africa.

Increasing fertilizer access and use is an essential component for improving crop production and food security in sub-Saharan Africa (SSA). However, given the heterogeneous nature of smallholder farms, fertilizer application needs to be tailored to specific farming conditions to increase yield, profitability, and nutrient use efficiency. The site-specific nutrient management (SSNM) approach initially developed in the 1990 s for generating field-specific fertilizer recommendations for rice in Asia, has also been introduced to rice, maize and cassava cropping systems in SSA. The SSNM approach has been shown to increase yield, profitability, and nutrient use efficiency. Yield gains of rice and maize with SSNM in SSA were on average 24% and 69% when compared to the farmer practice, respectively, or 11% and 4% when compared to local blanket fertilizer recommendations. However, there is need for more extensive field evaluation to quantify the broader benefits of the SSNM approach in diverse farming systems and environments. Especially for rice, the SSNM approach should be expanded to rainfed systems, which are dominant in SSA and further developed to take into account soil texture and soil water availability. Digital decision support tools such as RiceAdvice and Nutrient Expert can enable wider dissemination of locally relevant SSNM recommendations to reach large numbers of farmers at scale. One of the major limitations of the currently available SSNM decision support tools is the requirement of acquiring a significant amount of farm-specific information needed to formulate SSNM recommendations. The scaling potential of SSNM will be greatly enhanced by integration with other agronomic advisory platforms and seamless integration of digital soil, climate and crop information to improve predictions of SSNM recommendations with reduced need for on-farm data collection. Uncertainty should also be included in future solutions, primarily to also better account for varying prices and economic outcomes.

Spatial distribution of bed variables, animal welfare indicators, and milk production in a closed compost-bedded pack barn with a negative tunnel ventilation system.

This research aimed to characterize, evaluate and compare the spatial distribution of the leading bed variables, animal welfare indicators, and milk production in a closed compost-bedded pack barn (CBP) with a negative tunnel ventilation system, for summer and winter periods. The study was carried out in a CBP located in the Zona da Mata region, Minas Gerais, Brazil. The geostatistical modeling technique evaluated the variables of temperature, moisture content, and pH (on the surface and depth of 0.20m) across the length of the bed. Bed samples were characterized for carbon (C), nitrogen (N), and C:N ratio. Cows housed in the CBP were assessed for locomotion and hygiene scores and average milk production. To evaluate the thermoregulation of the cows, the respiratory rate (RR) and surface temperature (ST) were measured. Geostatistical analysis showed spatial dependence and the non-uniformity of the spatial distribution of bed variables. The worst levels of bed temperature and moisture were found in the regions close to the evaporative cooling plate, surrounding the feeding alley, and in the region with the highest cow stocking. The C:N ratio, obtained in both climatic seasons of the year, remained outside the recommended range for ideal composting. During the summer and winter, the bed variables' values suggest that the material was below levels for optimal composting; however, the aerated inner layer was biologically active. The high animal density significantly impacted the worsening of the bed moisture content and internal temperature. In general, dairy cows showed adequate hygiene (score of 1 and 2) and locomotion (score of 0 and 1) scores for the two climatic seasons evaluated, indicating good welfare conditions. In relation to RR and ST, the summer period presented less favorable environmental conditions. During winter, the average milk production was 28.1 ± 7.2 kg day-1, and during summer, it was 26.9 ± 6.7 kg day-1.

Assessment of the soil-erosion-sediment for sustainable development of South America.

One of the greatest threats to maintaining sustainable agro-ecosystems is mitigating the episodic soil loss from farm operations, further exacerbated by meteorological extremes. The Revised Universal Soil Loss Equation (RUSLE) is a model that combines the effects of rain, soil erodibility, topography, land cover, and conservation practices for estimating the annual average soil losses. This study aims to quantify soil water erosion to continental South America (S.A.) through RUSLE using available datasets and characterizing the average sediment delivery rate (SDR) to the major S.A. basins. Soil erodibility was estimated from the Global Gridded Soil Information soil database. LS-factor's topographical parameter was derived from Digital Elevation Models using the "Shuttle Radar Topography Mission" dataset. The R-factor was estimated from a previous study developed for S.A. and the C-factor from the Global Land Cover (Copernicus Global Land Services) database. We used a modeling study for SDR that simulated the annual average sediment transport in 27 basins in S.A. RUSLE set up presented a satisfactory performance compared to other applications on a continental scale with an estimated averaged soil loss for S.A. of 3.8 t ha-1 year-1. Chile (>20.0 t ha-1 year-1) and Colombia (8.1 t ha-1 year-1) showed the highest soil loss. Regarding SDR, Suriname, French Guyana, and Guyana presented the lowest values (<1.0 t ha-1 year-1). The highest soil losses were found in the Andes Cordillera of Colombia and the Center-South Region of Chile. In the former, the combination of "high" K-factor, "very high" C-factor, and "very high" LS-factor were the leading causes. In the latter, agriculture, livestock, deforestation, and aggressive R-factor explained the high soil loss. Basins with the highest SDR were located in the North Argentina - South Atlantic basin (27.73%), Mar Chiquitita (2.66%), Amazon River basin (2.32%), Magdalena (2.14%) (in Andes Cordillera), and Orinoco (1.83%).

Distribution of radiocesium and its controlling factors under the Japanese cedar canopies.

This study investigated the spatial distribution of radiocesium deposited by the Fukushima Daiichi Nuclear Power Plant accident in a densely planted Japanese cedar stand. Systematic grid sampling was conducted to determine 137Cs inventories in the layers of deposited organic material and mineral soil at two different spatial scales (hillslope [60 m2] and small [1 m2]). The results showed that 137Cs inventories along the hillslope were heterogeneously distributed, with coefficients of variation for the deposited organic material and mineral soil layers of 46.4% and 48.9%, respectively. The 137Cs inventory in each layer tended to show a lognormal distribution. The correlation between the 137Cs inventories in deposited organic material and mineral soil in the same sampling grid was weak. The controlling mechanisms of the 137Cs inventories in the litter and mineral soil layers differed due to differences in the underlying key processes, such as canopy-forest floor transfer due to hydrological and biological processes. No significant correlation was found between the distance from the nearest tree trunk and the 137Cs inventory in the deposited organic layer at each sampling point. In contrast, the 137Cs inventory in the soil tended to increase as the distance from the nearest tree trunk increased at both the hillslope and small scales. It was found that the initial spatial patterns of 137Cs in the soil layer due to atmospheric deposition were preserved in the cedar stand. Finally, we tested the effects of soil sampling density on the reliability of mean soil 137Cs inventory estimations in the cedar stand. The results indicated that a soil sampling area greater than 0.06 m2 at the hillslope scale and 0.008 m2 at the small scale enabled the mean 137Cs inventory to be estimated with an uncertainty of less than 20% in the cedar stand.

Effects of vegetation and terrain changes on spatial heterogeneity of soil C-N-P in the coastal zone protected forests at northern China.

Soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) are important indicators reflecting soil quality, and they can be used to effectively evaluate the effect of soil remediation. Many studies have evaluated the content of SOC, TN and TP in different ecosystems. However, after constructing protected forests for ecological restoration in the ecologically fragile coastal zone, the spatial distribution and influencing mechanism of SOC, TN and TP content is still uncertain. In this study, the spatial heterogeneity and influencing factors of SOC, TN and TP in surface (0-20 cm) soil were analyzed by traditional analysis and geostatistics. A total of 39 soil samples were collected under the coastal zone protected forest types including Quercus acutissima Carruth (QAC), Pinus thunbergii Parl (PTP), mixed PTP and QAC (QP) and Castanea mollissima BL (CMB) in the coastal zone protected forests in northern China. The results show that SOC, TN and TP content were defined as moderate variation, and they also show significant changes under different protected forest types (P < 0.05). The semivariance results indicate that SOC, TN and TP all exhibited strong spatial dependence class, with Range of 224 m, 229 m and 282 m respectively, which were more than the sampling scale of 200 m. The spatial prediction results showed that SOC, TN and TP content all appear in large areas of extremely low value in CMB, and its cross validation results showed that using vegetation and terrain factors as covariates in the spatial prediction of SOC, TN and TP can improve the prediction accuracy. The results of correlation analysis showed that the influencing factor for SOC and TN, and TP were NDVI and topographical changes, respectively. In general, vegetation and terrain factors as auxiliary factors can improved the accuracy of soil C-N-P spatial distribution prediction after afforestation in coastal zone.

Spatial distribution of heavy metals in soils of the flood plain of the Seversky Donets River (Russia) based on geostatistical methods.

Soil contamination by heavy metals (HM) is a worldwide problem for human health. To reduce risk to human health from exposure to toxic chemicals associated with soil contamination, it is necessary to monitor and assess HM concentrations in the soil for places where the concentration exceeds the acceptable levels. Spatial patterning is a necessary tool for assessment of the exposure risk of HM contamination. Soil sampling (n = 65) was carried out in technogenically polluted soils located at Rostov oblast to study the content and spatial distribution of four HM (Cu, Zn, Pb, and Cr) in the surface layer (0-20 cm) of the impact zone of former Lake Atamanskoe (floodplain of the Seversky Donets River valley, Rostov region) with an area of 3.91 km2. Extremely high values of HM concentrations were found with the maximum values of 702 mg/kg, 72,886 mg/kg, 2300 mg/kg, 259 mg/kg for Cu, Zn, Pb, and Cr, respectively. Inverse distance-weighted (IDW) interpolation was used to prepare 3D monoelement images of HM. Lognormal kriging and indicator kriging techniques were applied to create elemental spatial distribution maps and HM probability maps. The results showed that the total content of Cu, Zn, Pb, and Cr was moderately spatially dependent (nugget-to-sill ratio ranged from 31 to 38%), whereas the contamination index Zc formed strong spatial dependence patterns (nugget-to-sill ratio ranged from 0 to 21.4%). The obtained results of this study could serve as a guide to the authorities in identifying those areas which need remediation. Moreover, this study provides a tool for assessing the hygienic situation in the vicinity of Kamensk-Shakhtinsky (Rostov region) for decision making that can help to minimize the environmental risk of technogenic soil contamination of HM.

Distribution of rare earth elements in soils of contrasting geological and pedological settings to support human health assessment and environmental policies.

Establishing quality reference values (QRVs) for rare earth elements (REEs) in soils is essential for the screening of these emergent contaminants. Currently, Brazil has the second-largest reserve of REEs, but data regarding background concentrations and distributions in soils remain scarce. The aim of this study was to establish the QRVs and assess the spatial distribution of REEs in soils, including REE fractionations and anomalies in (Piauí) state (251,529.186 km2), northeastern Brazil. This study reports the most detailed data on REE geochemistry in Brazilian soils. A total of 243 composite soil samples was collected at 0-20 cm depth. The mean background concentrations in soils followed the abundance of the earth's upper crust: Ce > La > Nd > Pr > Sm > Dy > Gd > Er > Yb > Eu > Tb > Lu. The ∑REEs (mg kg-1) showed the following order based on the individual mesoregions of Piauí state: Southeast (262.75) > North and Central-North (89.68) > Southwest (40.33). The highest QRVs were observed in the Southeast mesoregion. The establishment of QRVs based on the mesoregion scale improves data representativeness and the monitoring of natural REE values by identifying hot spots. Geostatistical modeling indicated significant local variability, especially in the Southeast mesoregion. The levels of these elements in this spatial zone are naturally higher than the other values across Piauí state and the mesoregion itself and indicate a high potential to exceed the QRVs. Our approach provides much needed data to help strengthen policies for both human health and environmental protection.

Spatial variability of soil nutrients in apple orchards and agricultural areas in Kinnaur region of cold desert, Trans-Himalaya, India.

The mountain ecosystem is highly vulnerable to climate changes fraught with a multitude of problems related to environment, food, and nutritional security. Quantification of the soil fertility status can provide an efficient way to devise strategies for sustainable crop production. The lack of information on the soil fertility status prompted us to delineate the spatial variability of the soil attributes, viz., pH, electrical conductivity (EC), soil organic carbon (OC), and the macronutrients (nitrogen (N), phosphorus (P), and potassium (K)). The extensive soil sampling was carried out from the apple orchards (AO) and potential areas under agricultural land (AL) in Kinnaur region of cold desert, Trans-Himalaya, India. Descriptive statistics was employed for the exploratory analysis of data representing a wide variation (coefficient of variation, CV = 5.70-58.62%). The available N and P, categorized as low (< 280 kg ha-1) to medium (280-560 kg ha-1) and low (4-10 kg ha-1) to high (> 25 kg ha-1), respectively, were the main limiting factors in crop production. The availability of the K was categorized as medium (118-280 kg ha-1) to high (> 280 kg ha-1). The geostatistical analysis was carried out to check the spatial dependency in the dataset. The principal component analysis (PCA) was carried out and the dominant PCs were used in fuzzy c-means clustering for the delineation of management zones (MZs). The management zones highlight the need for area-specific interventions for ameliorating soil degradation and increasing apple productivity. The soil nutrient maps in spatial scale would help to provide precise fertilizer recommendations for sustainable production and environmental conservation.

Occurrence of some rare earth elements in vineyard soils under semiarid Mediterranean environment.

A comprehensive investigation has been carried out into the concentrations of a range of REEs (neodymium Nd, cerium Ce, lanthanum La, yttrium Y, scandium Sc) in soils of vineyards belonging to the protected denomination of origin (PDO) Valdepeñas (Central Spain). The mean concentrations (expressed in mg kg-1) are Ce 70.6, Nd 32.9, La 36.2, Y 21.6, and Sc 13.7 in surface horizons (Ap), while in subsurface horizons (Bt or Bw and some Ck), the values are Ce 67.6, Nd 31.8, La 34.4, Y 19.6, and Sc 13.9. The relative abundance in these soils is Ce > La > Nd > Y > Sc in both the surface and subsurface horizons. These values are close to, or slightly higher than, the regional levels but similar to national and global averages, although relatively high values have been detected at certain sampling points. Another aim was to explain the spatial variations in these elements within the territory under study. It was found that the spatial variations are due to the nature of the parent materials and the pedogenetic processes, although the sparse spatial distribution patterns with prominent anomalies are interpreted arising from anthropogenic sources (fertilization). However, these anomalies did not present any environmental risk in the studied zone.