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FedLens is the federated learning model based on viewing world tourist spots virtually in a privacy-preserving manner. We define Virtual Tourism as enjoying the natural beauty, other related activities online, using AR/VR/MR technology-based 'virtual eye', to interact actively with nature and people at tourist spots. Federated learning-based mobile crowdsensing is an emerging collaborative distributed learning paradigm for privacy-preserving, energy-efficient, and scalable networks. Edge intelligent mobile crowdsensing uses geotagged tourist attractions. The purpose of this study is to explore the geo-statistics of tourist areas. The proposed 'FedLens' brings tourists closer to the interests using augmented reality through the virtual guide. ArcGIS software maps a tourist area. 5G mobile crowdsensing helps to explore unknown tourist spots in real time. 'FedLens' provides a privacy-preserving incentive mechanism to encourage reliable contributors to get better Quality of Information. The average global data aggregation time is approximately 12%. The contributor's collection time is 88% of the total processing time. The contributors use multifaceted intelligent federated computing to provide detailed geospatial information and promote sustainable ecotourism. Augmented reality-based virtual tourism ecosystem development is the ultimate goal of this work to attract more virtual tourists for a sustainable environment. Future physical tour-planning recommendation systems are incorporated in the proposed model. [ABSTRACT FROM AUTHOR]

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[Objective] To study the evolution characteristics of available phosphorus (AP) content in the surface soil of Guangxi from 1984 to 2019, providing scientific basis for rational formulation of crop fertilization management measures and improvement of soil fertility. [Method] Based on the data of Guangxi second soil survey project (1984), Guangxi formula fertilization project (2009) and the long-term positioning experiment data (2010-2019), geostatistics, dynamic attitude and distribution index were used to analyze the spatio-temporal evolution of AP content in surface soil in Guangxi and its distribution at different altitudes. [Result] From 1984 to 2019, the overall AP content in the surface soil of Guangxi has greatly increased. In 1984, the surface soil with low AP content (less than 10.0 mg/kg) accounted for about 80.00% of the total soil area of Guangxi. In 2019, the surface soil AP content was concentrated in the soil area of 10.0-40.0 mg/kg, accounting for 97.00% of the total soil area of Guangxi. The proportion of soil area classified as grade 2(20.0 mg/kg < AP content ≤40.0 mg/kg) and grade 3 (10.0 mg/kg < AP content ≤20.0 mg/kg) increased from 2. 47% and 4. 12% in 1984 to 43.38% and 48.57% in 2019, respectively. In terms of the rate of change, the soil area with grade 6(AP content ≤3.0 mg/kg) had the most rapid decrease with a dynamic index of 2.82%. On the other hand, the soil area with grade 2 had the most significant increase with a dynamic index of 47. 28%. The overall rate of change showed the following order: grade 2 > grade 3 > grade 1 (AP content >40.0 mg/kg) > grade 6 > grade 5 (3.0 mg/kg grade 4(5.0 mg/kg < AP content ≤10.0 mg/kg). At different altitudes, the distribution index curves of soil areas with grade 1 and grade 6 experienced significant changes. In 1984, the distribution index of soil area with grade 1 at five elevation zones was 0.07,2.00,2. 22,1.69 and 0. 16, respectively. In 2019, the distribution index changed to 2. 66, 0.25,0,0 and 0, respectively. Similarly, in 1984, the distribution index of soil area with grade 6 at the five elevation zones was 1.33, 1. 15,0.96,0.80 and 0.90, respectively. In 2019, the distribution index changed to 0. 12,0.08, 1.94,7.95 and 1.76, respectively. [Conclusion] From 1984 to 2019, significant changes occurred in the level and spatial distribution pattern of AP content in the surface soil of Guangxi. In most areas, the AP content in the surface soil has significant increased and is mainly concentrated between 10.0 to 40.0 mg/kg. Soil type and altitude are important influencing factors, and human interference is among the direct reasons leading to the significant increase in AP content in the surface soil. Therefore, in the process of crop production in Guangxi, scientific fertilization should be further promoted, the proportion of organic fertilizer application should be increased, and the efficiency of phosphate fertilizer application should be improved. [ABSTRACT FROM AUTHOR]

This study evaluates the spatial equity of the educational service provided in the urban areas of Manizales and Villamaría based on the hypothesis that there is inequity in the provision of this service. Methodology involves GIS-based territorial accessibility analysis using average travel times from a network of transport infrastructures as well as the geographical location of all formal education establishments. This methodology is designed to be applied on different locations around the world. As the main results, the areas with mixed values concerning educational provision are in the lower strata, which directly correlates with low spatial educational equity rates.

Cleistogenes songorica, as a clustered grass, is the main grassland flora of the Stipa breviflora desert grassland. Some studies have shown that the constructive species of S. breviflora (sparse cluster type) is prone to cluster fragmentation; however, research on C. songorica is relatively rare. Then will the C. songorica plant population (dense cluster type) also have cluster fragmentation under the influence of intense grazing? To answer this question, we used variance analysis and geo‐statistical methods. The spatial distribution of C. songorica in S. breviflora desert steppe in Inner Mongolia was measured under four grazing intensities (no grazing, CK, 0 sheep·ha−1·half year−1; light grazing, LG, 0.93 sheep·ha−1·half year−1; moderate grazing, MG, 1.82 sheep·ha−1·half year−1; and heavy grazing, HG, 2.71 sheep·ha−1·half year−1) and four scales (10 cm × 10 cm, 20 cm × 20 cm, 25 cm × 25 cm, 50 cm × 50 cm). We then analyzed C. songorica whether fragmentation was present. The results showed that increased grazing intensity is associated with increased density and decreased height, coverage, and standing crop of C. songorica. The spatial distribution of C. songorica was affected by structural factors, and spatial heterogeneity decreased with increased spatial scale. With increased grazing intensity and spatial scale, the patch area of C. songorica gradually increased and tended toward band distribution. In summary, increased grazing intensity and spatial scale led to weakened heterogeneity of C. songorica spatial distribution and increased consistency. [ABSTRACT FROM AUTHOR]

This study evaluates the spatial equity of the educational service provided in the urban areas of Manizales and Villamaría based on the hypothesis that there is inequity in the provision of this service. Methodology involves GIS-based territorial accessibility analysis using average travel times from a network of transport infrastructures as well as the geographical location of all formal education establishments. This methodology is designed to be applied on different locations around the world. As the main results, the areas with mixed values concerning educational provision are in the lower strata, which directly correlates with low spatial educational equity rates. [ABSTRACT FROM AUTHOR]

Abstract Cleistogenes songorica, as a clustered grass, is the main grassland flora of the Stipa breviflora desert grassland. Some studies have shown that the constructive species of S. breviflora (sparse cluster type) is prone to cluster fragmentation; however, research on C. songorica is relatively rare. Then will the C. songorica plant population (dense cluster type) also have cluster fragmentation under the influence of intense grazing? To answer this question, we used variance analysis and geo‐statistical methods. The spatial distribution of C. songorica in S. breviflora desert steppe in Inner Mongolia was measured under four grazing intensities (no grazing, CK, 0 sheep·ha−1·half year−1; light grazing, LG, 0.93 sheep·ha−1·half year−1; moderate grazing, MG, 1.82 sheep·ha−1·half year−1; and heavy grazing, HG, 2.71 sheep·ha−1·half year−1) and four scales (10 cm × 10 cm, 20 cm × 20 cm, 25 cm × 25 cm, 50 cm × 50 cm). We then analyzed C. songorica whether fragmentation was present. The results showed that increased grazing intensity is associated with increased density and decreased height, coverage, and standing crop of C. songorica. The spatial distribution of C. songorica was affected by structural factors, and spatial heterogeneity decreased with increased spatial scale. With increased grazing intensity and spatial scale, the patch area of C. songorica gradually increased and tended toward band distribution. In summary, increased grazing intensity and spatial scale led to weakened heterogeneity of C. songorica spatial distribution and increased consistency.

As one of the most catastrophic atmospheric events, drought affects various aspects of the environment. Groundwater resources are one of the sectors that are influenced by long-term droughts and have received insufficient attention compared to other aspects of the environment. Rainfall data collected by 23 metrological stations for 20 years (2005, 2010, 2015, and 2020) was used to investigate the drought event and its situation in Qazvin Plain, the central plateau of Iran. Drought characteristics are evaluated using the 24-month standardized precipitation index (SPI). The results of SPI indicated that insufficient precipitation, excessive use of groundwater for irrigation, and utilization of uncontrolled wells caused a significant reduction in groundwater aquifers from 2015 to 2020. To assess the performance of the SPI, a five-year moving average of available precipitation data was calculated, and the result confirmed the outcomes of the SPI. Appropriate geostatistical interpolation methods are used to generate maps of drought zoning. Based on the results of this investigation in the northeastern part of the study area, June and November had the highest and lowest rates of drought, respectively. The linear regression between the annual average of precipitation and the changes in groundwater aquifer level exposed a significant correlation of R² = 0.4253. Furthermore, linear regression between 24-month SPI and groundwater aquifer level indicated a correlation of R² = 0.614. Considering the results of this study, the reduction of groundwater aquifer levels in Qazvin Plain from 2015 to 2015 exposed a significant negative difference compared to previous years (2005 to 2010). [ABSTRACT FROM AUTHOR]

Necessity calls for the environmental aspects of groundwater to be evaluated and properly managed based on the observed spatial distribution with respect to quality, as it contributes to a significant portion of average water usage globally. Variations in groundwater quality in the Ibadan Metropolis might be a result of physical and chemical trends in the region leading to a decline in quality. The study was geared towards the spatial evaluation of groundwater quality using factor analysis and the Kriging algorithm. The parameters examined include pH, electrical conductivity, total dissolved solids, carbonates, chloride, nitrate, sulphate, calcium, sodium, magnesium, and potassium, which were sampled and analysed from the existing municipal deep wells in the Ibadan Metropolitan area; and distribution maps of each parameter were created using a geostatistical approach. Factor analysis examined the relationship between human activities and concentration levels. Semi-variograms were tested to ascertain the best-fitted model accuracy measures, average standard error, root mean square error, and root mean square error standardised. The groundwater index was calculated to ascertain the drinkability of the water in the study area. Overall, the result shows that the groundwater in the study area is suitable for consumption; drinking, and other uses. Kriging is a suitable assessment tool for modelling environmental parameters. HIGHLIGHTS Geo-statistics was adopted to model the spatial distribution of concentration levels of parameters.; Factor analysis was employed to elucidate the predominant lithological effect.; The GWQI assessed groundwater suitability in the study area.; Importance of geostatistical techniques in water quality modelling was presented.; Importance of research to individuals and relevant agencies was presented.;

The aim of this research was to evaluate the status of selected soil chemical properties and to explore their spatial variability in various agroecosystems (AEs) of Choke Mountain, northwestern highlands of Ethiopia. A total of 74 geo-referenced composite surface (0-30 cm) soil samples were collected from representative farm lands. Soil chemical properties were analyzed following standard procedures. In addition, semi-variogram model was applied to estimate the spatial variability of soil properties. The results showed that soil chemical properties are below their threshold values in all agroecosystems and soil groups. The value of soil pH ranged from 4.3 to 6.8; Exchangeable acidity ranged from 0.124 to 0.180 cmol (+) kg−1; EC ranged from 0.02 to 0.19 dS m−1; OC ranged from 0.7 to 3.8%; TN ranged from 0.11 to 0.27%; Exchangeable potassium from 0.29 to 2.0 cmol (+) kg−1and CEC from 22 to 56 cmol (+)kg−1. The results also revealed that soil pH, exchangeable acidity, electrical conductivity, and exchangeable potassium showed strong spatial dependence whereas SOC and TN had moderate spatial dependence. The results indicated that soil quality is deteriorating. Hence, crop, soil and site-specific soil management strategies, primarily focusing on improving the content of SOM and addressing the problems of soil acidity are suggested to improve the soil conditions.

Abstract Background Grazing disturbance plays an important role in the desert steppe ecosystem in Inner Mongolia, China. Previous studies found that grazing affected the spatial distribution of species in a community, and showed patchiness characteristics of species under different grazing treatments. Artemisia frigida is the dominant species and semi-shrub in desert steppe, and whether grazing interference will affect the spatial distribution of A. frigida is studied. In this study, geo-statistical methods were mainly used to study the spatial distribution characteristics of A. frigida population in desert steppe of Inner Mongolia at two scales (quadrat size 2.5 m × 2.5 m, 5 m × 5 m) and four stocking rates (control, CK, 0 sheep·ha–1·month–1; light grazing, LG, 0.15 sheep·ha–1·month–1, moderate grazing, MG, 0.30 sheep·ha–1·month–1, heavy grazing, HG, 0.45 sheep·ha–1·month–1). Results The results showed that the spatial distribution of A. frigida tended to be simplified with the increase of stocking rate, and tended to be banded with increased spatial scale. The density and height of A. frigida increased with increasing scale. With increased stocking rate, the density of A. frigida population decreased linearly, while its height decreased in a step-wise fashion. The spatial distribution of A. frigida was mainly affected by structural factors at different scales and stocking rate. The density of A. frigida was more sensitive to change in stocking rate, and the patchiness distribution of A. frigida was more obvious with increase in scale. Conclusions Stocking rate has a strong regulatory effect on the spatial pattern of A. frigida population in the desert steppe. Heavy grazing reduced the spatial heterogeneity of A. frigida in the desert steppe. The smaller dominant populations are unfavourable for its survival in heavy grazing condition, and affects the stability and productivity of the grassland ecosystem.

For the pollution assessment and quantitative source apportionment of heavy metals in surface dust, a total of 52 surface dust samples were collected from bus stops in Tianshui City. The geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to analyze the pollution levels caused by heavy metals. The Positive Matrix Factorization (PMF) of the receptor modeling and geo-statistics were employed to analyze the source of the heavy metals. The results were as follows. ① Except for Mn, Co and V, the mean concentrations of other heavy metals have exceeded the local background value of Gansu. The percentage of excessive concentrations of Cu, Zn, Sr, Ba and Pb in the samples was 100%, and that of Cr, Ni and As were 96.15%, 94.23%, and 96.15%, respectively. ② Semivariogram model fitting showed that the block-based coefficients of Cu, Zn, Sr, Ba, Pb, Cr, Ni, and As were between 0.25 and 0.75, indicating that they were mainly affected by human factors. The high values of Pb, Zn, Ni and As were mainly distributed in the eastern part of the study area, and the high values of Cu, Sr, Ba and Cr were distributed in a spot-like pattern in the study area. ③ The Igeo results showed that As, Cu, Zn, and Pb were the main contamination factors, and the optimized RI showed that the heavy metals were the overall ecological risk of intensity, among which Pb, As and Cu were the main ecological factors and should be taken as the priority control objects. ④ Based on the PMF, there are four main sources of eleven heavy metals. V, Mn, and Co were attributed to natural sources, accounting for 18.33%; Cu, Sr, and Ba were from mixed sources of pollution from transportation and industrial alloy manufacturing, accounting for 26.99%; Cr and Ni were from sources of construction waste pollution, accounting for 17.17%, As, Zn and Pb were mainly produced by coal-traffic mixed pollution emissions, accounting for 37.52%. Overall, the study area was dominated by coal-traffic emissions. [ABSTRACT FROM AUTHOR]

A study was conducted at Kalamandari Tanda-1 micro-watershed, Karnataka, India, to assess the spatio-temporal variability of surface soil properties sampled from 23 varied soil-phase units, delineated using IRS P6 LISS-IV merged Cartosat-I imagery (2.5 m spatial resolution). Three sets of surface soil samples (231 in each set) were collected, analyzed, and interpolated to evaluate the soil spatio-temporal variability. Further soil-phase-wise composite representative samples (23 in each set) were collected before the sowing and after the harvest of pigeon peasin January 2019 and 2020. Results showed that the soil OC with N, Mg with Ca, exchangeable sodium percentage (ESP), and cation exchange capacity (CEC) with Mn and K2O have a significantly positive association (p < 0.01) during both years. Similarly, a significantly positive association (p < 0.05) was observed among soil OC and N with Cu, Mn with CEC, and Zn with Fe, while a negative association was seen between available P2O5 and Na. Skewness from descriptive statistics revealed that pH, EC, Cu, Mg, B, and ESP distribution varied temporally, whereas other parameters remained almost unchanged. Geo-statistically, the spatial distribution of measured soil properties was best fitted to exponential, stable, and K-Bessel models. The available N in 2018 and the ESP in 2019 have shown weak spatial dependency, whereas the rest of the soil parameters showed moderate and strong spatial dependency. These interpolated maps were exported as vector layers to quantify soil-phase-wise spatio-temporal variability of soil properties. [ABSTRACT FROM AUTHOR]

This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.

Species diversity has spatial heterogeneity in ecological systems. Although a large number of studies have demonstrated the influence of soil properties on species diversity, most of them have not considered their spatial variabilities. To remedy the knowledge gap, a 1 ha (100 m × 100 m) plots of arid desert riparian forest was set up in the Ebinur Wetland Nature Reserve (ELWNR) in the NW China. Then, the minimum data set of soil properties (soil MDS) was established using the Principal Component Analysis (PCA) and the Norm Value Determination to represent the total soil property data set (soil TDS). The Geo-statistics and two models (i.e., Random Forest/RF and Multiple Linear Regression/MLR) were used to measure the spatial variability of species diversity, and predict its spatial distribution by the soil MDS, respectively. The results showed that the soil MDS was composed of soil salt content (SSC), soil total phosphorus (STP), soil available phosphorus (SAP), soil organic carbon (SOC) and soil nitrate nitrogen (SNN); which represented the soil TDS perfectly (R2 =0.62). Three species diversity indices (i.e., Shannon-Wiener, Simpson and Pielou indices) had a high spatial dependence (C0/(C0+C)< 25%; 0.72 m = range= 0.77 m). Ordinary kriging distribution maps showed that the spatial distribution pattern of species diversity predicted by RF model was closer to its actual distribution compared with MLR model. RF model results suggested that the soil MDS had significant effect on spatial distribution of Shannon-Wiener, Simpson and Pielou indices (Varex= 56%, 49% and 36%, respectively). Among all constituents, SSC had the largest contribution on the spatial variability of species diversity (nearly 10%), while STP had least effect (< 5.3%). We concluded that the soil MDS affected spatial variability of species diversity in arid desert riparian forests. Using RF model can predict spatial variability of species diversity through soil properties. Our work provided a new case and insight for studying the spatial relationship between soil properties and plant species diversity. [ABSTRACT FROM AUTHOR]

Background: Grazing disturbance plays an important role in the desert steppe ecosystem in Inner Mongolia, China. Previous studies found that grazing affected the spatial distribution of species in a community, and showed patchiness characteristics of species under different grazing treatments. Artemisia frigida is the dominant species and semi-shrub in desert steppe, and whether grazing interference will affect the spatial distribution of A. frigida is studied. In this study, geo-statistical methods were mainly used to study the spatial distribution characteristics of A. frigida population in desert steppe of Inner Mongolia at two scales (quadrat size 2.5 m × 2.5 m, 5 m × 5 m) and four stocking rates (control, CK, 0 sheep·ha–1·month–1; light grazing, LG, 0.15 sheep·ha–1·month–1, moderate grazing, MG, 0.30 sheep·ha–1·month–1, heavy grazing, HG, 0.45 sheep·ha–1·month–1). Results: The results showed that the spatial distribution of A. frigida tended to be simplified with the increase of stocking rate, and tended to be banded with increased spatial scale. The density and height of A. frigida increased with increasing scale. With increased stocking rate, the density of A. frigida population decreased linearly, while its height decreased in a step-wise fashion. The spatial distribution of A. frigida was mainly affected by structural factors at different scales and stocking rate. The density of A. frigida was more sensitive to change in stocking rate, and the patchiness distribution of A. frigida was more obvious with increase in scale. Conclusions: Stocking rate has a strong regulatory effect on the spatial pattern of A. frigida population in the desert steppe. Heavy grazing reduced the spatial heterogeneity of A. frigida in the desert steppe. The smaller dominant populations are unfavourable for its survival in heavy grazing condition, and affects the stability and productivity of the grassland ecosystem. [ABSTRACT FROM AUTHOR]

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide ( NO 2 ) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO 2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO 2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods. [ABSTRACT FROM AUTHOR]

This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM. [ABSTRACT FROM AUTHOR]

For the pollution assessment and quantitative source apportionment of heavy metals in surface dust, a total of 52 surface dust samples were collected from bus stops in Tianshui City. The geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to analyze the pollution levels caused by heavy metals. The Positive Matrix Factorization (PMF) of the receptor modeling and geo-statistics were employed to analyze the source of the heavy metals. The results were as follows. ① Except for Mn, Co and V, the mean concentrations of other heavy metals have exceeded the local background value of Gansu. The percentage of excessive concentrations of Cu, Zn, Sr, Ba and Pb in the samples was 100%, and that of Cr, Ni and As were 96.15%, 94.23%, and 96.15%, respectively. ② Semivariogram model fitting showed that the block-based coefficients of Cu, Zn, Sr, Ba, Pb, Cr, Ni, and As were between 0.25 and 0.75, indicating that they were mainly affected by human factors. The high values of Pb, Zn, Ni and As were mainly distributed in the eastern part of the study area, and the high values of Cu, Sr, Ba and Cr were distributed in a spot-like pattern in the study area. ③ The Igeo results showed that As, Cu, Zn, and Pb were the main contamination factors, and the optimized RI showed that the heavy metals were the overall ecological risk of intensity, among which Pb, As and Cu were the main ecological factors and should be taken as the priority control objects. ④ Based on the PMF, there are four main sources of eleven heavy metals. V, Mn, and Co were attributed to natural sources, accounting for 18.33%; Cu, Sr, and Ba were from mixed sources of pollution from transportation and industrial alloy manufacturing, accounting for 26.99%; Cr and Ni were from sources of construction waste pollution, accounting for 17.17%, As, Zn and Pb were mainly produced by coal-traffic mixed pollution emissions, accounting for 37.52%. Overall, the study area was dominated by coal-traffic emissions.

The aim of this research was to evaluate the status of selected soil chemical properties and to explore their spatial variability in various agroecosystems (AEs) of Choke Mountain, northwestern highlands of Ethiopia. A total of 74 geo-referenced composite surface (0-30 cm) soil samples were collected from representative farm lands. Soil chemical properties were analyzed following standard procedures. In addition, semi-variogram model was applied to estimate the spatial variability of soil properties. The results showed that soil chemical properties are below their threshold values in all agroecosystems and soil groups. The value of soil pH ranged from 4.3 to 6.8; Exchangeable acidity ranged from 0.124 to 0.180 cmol (+) kg−1; EC ranged from 0.02 to 0.19 dS m−1; OC ranged from 0.7 to 3.8%; TN ranged from 0.11 to 0.27%; Exchangeable potassium from 0.29 to 2.0 cmol (+) kg−1and CEC from 22 to 56 cmol (+)kg−1. The results also revealed that soil pH, exchangeable acidity, electrical conductivity, and exchangeable potassium showed strong spatial dependence whereas SOC and TN had moderate spatial dependence. The results indicated that soil quality is deteriorating. Hence, crop, soil and site-specific soil management strategies, primarily focusing on improving the content of SOM and addressing the problems of soil acidity are suggested to improve the soil conditions. [ABSTRACT FROM AUTHOR]

Knowledge and up-scaling of status, availability, and distribution of soil micronutrients are hugely significant for enhancing crop productivity and profitability. However, regional database entailing spatial variability of key micronutrients for a major apple-producing region like the Kashmir Himalayan Region (KHR) is missing. We investigated 588 topsoil samples between 2016 and 2017 (to 30 cm depth) to estimate spatial variability of extractable Zn, Cu, Mn, and Fe; develop spatial maps; and delineate potential management zones (MZs) in apple orchards using geo-statistical methods. Soil sampling was performed post-harvest season. Principal component analysis and fuzzy c-mean clustering were performed to develop MZs. Results exhibited wide variation, with high coefficient of variation (%) for Mn and Cu (123.9 and 114.4, respectively) and low (71.6) and medium (104) variability for Fe and Zn. Mean concentrations followed the order: Fe > Mn > Cu > Zn, with deficiencies evident towards central and northern regions of the study area. The best-fitted model was exponential. The nugget/sill ratio values were 0.41, 0.08, 0.37, and 0.38 for Zn, Cu, Mn, and Fe, respectively, indicating strong spatial dependence for Cu and moderate dependence for Fe, Zn, and Mn. Correlations between elevation, slope, and soil pH with micronutrients were negative, whereas with soil organic carbon and electrical conductivity positive relationships were identified. Three principal components accounted for 67·18% data variance. Based on the fuzzy performance index and modified partition entropy, five MZs were delineated, which exhibited variations from each other. These MZs highlighted the need for specific supplemental provisions in increasing soil fertility and apple productivity. [ABSTRACT FROM AUTHOR]

Soil moisture has been a key limiting factor for crop growth during the surface process in many lands. It is very necessary to establish the placement criteria of monitoring sites for the soil moisture in optimum irrigation. The spatial and temporal distribution of Available Water Capacity (AWC) was strongly correlated with soil moisture. The AWC spatial distribution pattern was also related to soil characteristics, but it can be more stable than that of soil moisture. In this study, a spatially stratified sampling was proposed to relieve the strong temporal and spatial variability, when the soil moisture was used as a variable. The Stratified Sampling method based on spatial autocorrelation of Auxiliary Variables (SSAV) was also used to combine the classical statistics and geo-statistics, where the AWC was introduced as an auxiliary variable. The experiments were then carried out to verify at a field scale. The results showed that the AWC in the 0-40 and 0-80 cm soil layers followed the normal distribution, indicating a moderate variation. In the 90% confidence interval, the number of monitoring points in the 0-40 and 0-80 cm soil layers in the study area was 7 and 6, respectively, where the sampling error was 10%, indicating that the reducing number of monitoring points, and cost-saving monitoring of soil moisture. The geostatistical analysis demonstrated that the range of two soil layers (0-40 and 0-80 cm) was both 366 m in the semi-variance function of AWC. The relative errors of soil moisture estimated by the Simple Random Sampling (SRS) and SSAV were –27.03%-52.38%, and –23.23%-35.15%, respectively. The relative error of soil moisture estimated by the SSAV was reduced by 26.48%, compared with the SRS. The mean standard deviation was 4.78%, 17.30% lower than that of SRS. A paired t-test indicated that the relative error and the mean standard deviation of soil moisture were 9 times in the two soil layers under two monitoring during 2016-2018. Thus, there were significant differences between the relative error range and the mean standard deviation under two monitoring (P<0.001). Moreover, the uncertainty of SSAV was reduced significantly, whereas, the estimation accuracy was improved significantly, compared with the SRS. Among them, the uncertainty of SRS was attributed to the independent samples that followed the normal distribution. There was also a certain spatial change of soil characteristics at a certain scale, indicating a spatial correlation. Correspondingly, the larger deviation of estimation accuracy was attributed to the SRS model without considering the spatial autocorrelation of soil moisture. The Root Mean Square Error (RMSE) between the observed and estimated values was only 0.010 4 cm3/cm3, indicating significantly lower than that of the SRS (0.012 0 cm3/cm3). As such, the uncertainty of sampling was reduced according to the value range of AWC. The new sampling was fully considered the influence of the spatial structure of the target variable on the layout of monitoring points. Specifically, the distance between any two monitoring points was required to be greater than the range, where the monitoring points were independent of each other. Therefore, the estimation accuracy and precision of soil moisture were improved, compared with the SRS. Consequently, the SSAV can be widely expected to serve a time-, labor- and cost-saving monitoring scheme for the average soil moisture. The finding can provide a promising guideline for water resources management and water use efficiency in modern agriculture. [ABSTRACT FROM AUTHOR]

Sulfur is an essential, fourth major nutrient required by plant after nitrogen, phosphorus and p otassium. The Potohar plateau covers an area of more than one million hectares. Nitrogen and phosphorus deficiency is well documented in Potohar plateau. Preliminary surveys suggested plant available SO4-S deficiency in Potohar Plateau, but no work has been carried out for accounting spatial variability of plant available SO4-S. A field survey was conducted in the two districts (Attock and Chakwal) of Potohar region for mapping plant available sulfate sulfur content in the soils. One hundred and fifty-one farmers' fields growing oilseed crops {Mustard (Brassica napus) and Sarson (Brassica juncea)} i.e., 78 from district Attock and 72 from district Chakwal were sampled. Soil sampling was done at the depth of 0-20 cm with stainless steel augur. Georeferencing of the sampled location was carried out using GPS (Garmine etrex). Spatial data along with attribute data was used to carry out geo-statistical analyses. The results indicated a widespread SO4-S deficiency in both districts i.e., 85 and 95% of total analyzed soil samples were categorized as deficient in plant available SO4-S contents in district Chakwal and Attock, respectively. Plant available sulfur was strongly spatial dependent in both districts which provided an opportunity to prepare digital maps indicating differential nutrient status of both districts. [ABSTRACT FROM AUTHOR]

In recent years, due to the expansion of industrial activities, the concentration of heavy metals in the environment as well as foods has increased. Heavy metals are dangerous because of their bioaccumulation. Regarding the importance of contamination of heavy elements of the soil, the present study aimed to provide the spatial distribution of heavy metals and its relationship with land use in the Yazd-Ardakan plain, Iran. First, 201 soil samples from depths of 0 to 20 cm were sampled using the hypercube method, and the total concentration of iron, manganese, nickel, lead, and zinc elements were determined using Analytical Jena-novAA300 atomic absorption device. Then, to convert point data to surface data, geostatistical methods of IDW, GPI, RBF, LPI, and Kriging were used. The land cover/use map of the Yazd-Ardakan plain in 2016 was mapped using an object-oriented classification method. Results of the relationship between heavy metals concentration and land cover/use showed that the agricultural lands and gardens and sand dunes with the mean of 0.950 and 0.836 ppm had the highest and lowest iron concentrations. The highest mean concentration of manganese was related to the residential land (1.821 ppm) and the lowest mean of rocky terrains (1.083 ppm), the most average for poor rangelands and bare land was (0.302 ppm), Residential areas had the lowest nickel concentration (0.192 ppm). The highest mean of lead metal in agricultural land and gardens, as well as residential areas (1.465 and 1.373 ppm, respectively) and the lowest, mean in rocky terrains (0.925 ppm). Agricultural and gardens areas, and residential lands, have the highest mean of zinc concentration (0.583 and 0.552 ppm, respectively), and the rocky terrain has the lowest zinc concentration (0.342 ppm).

Almost all causative factors of diseases depend on location. The Digital Earth approach is suitable for studying diseases globally. Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors. Through modelling, the most important causative factors can be extracted and the epidemiology of the disease can be observed. In this paper, skin cancer (the most common type of cancer) has been modelled based on its causative factors, including climate factors, people's occupations, nutrition habits, socio-economic factors, and usage of chemical fertiliser. To fit the model, a data framework was first designed, and then data were gathered and processed. Finally, the disease was modelled using Generalised Linear Models (GLM), a statistical model based on the location of the factors. The results of this study identify the most important causative factors together with their relative priority. Furthermore, a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors. This work illustrates the ability of the model to predict disease occurrence. Thus, by using this Digital Earth approach, skincancer can be studied in all the key countries around the world.

Ebola virus (EBV) disease is a globally acknowledged public health emergency, endemic in the west and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in the Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impact of reported violence on the spread of the virus. Using fully Bayesian geo‐statistical analysis through stochastic partial differential equations (SPDE) allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation was based on the integrated nested Laplace approximation (INLA). Our findings revealed a positive association between violent events in the affected areas and the reported EBV cases (posterior mean = 0.024, 95% CI: 0.005, 0.045) and deaths (posterior mean = 0.022, 95% CI: 0.005, 0.041). Translating to an increase of 2.4% and 2.2% in the relative risks of EBV cases and deaths associated with a unit increase in violent events (one additional Ebola case is associated with an average of 45 violent events). We also observed clusters of EBV cases and deaths spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hot spot identification, location‐specific disease surveillance and intervention. [ABSTRACT FROM AUTHOR]

When irrigation management is not properly conducted in irrigation areas, especially in semi-arid regions, soil may be salinized and sodiumized, with decrease in agricultural productivity. Investigation of spatial variation of the factors that condition the processes is extremely important in agricultural planning. Current analysis mapped the space variability of salinity and sodiumized in an area with irrigated mango trees in the Vale do Açu (RN), Brazil. Statistical and geostatistical techniques were applied by GeoR and Vesper software. The sampling grid was performed in a rectangular area of 100 m x 64 m (6,400 m²), points distanced 16 m x 10 m, totaling 40 samples. Variables Ca2+, Mg2+, K+, Na+, pH, CE, PST and RAS were determined. Results indicate that the area has low risk in salinity and sodiumization. Attributes behaved heterogeneously, with the exception of K+ and pH presenting a low variation coefficient. Spatial dependence was weak for Na+, K+ and RAS, moderate for attributes pH, CE and PST, and strong for Ca and Mg only. Geo-statistics proved to be a tool capable of delimiting the areas with higher and lower concentrations in unsampled sites, contributing to the rational use of soil and water. [ABSTRACT FROM AUTHOR]

As one of the most catastrophic atmospheric events, drought affects various aspects of the environment. Groundwater resources are one the sectors that are influenced by long-term droughts and received insufficient attention compared to other aspects of the environment. Rainfall data collected by 23 metrological stations for 20 years (2005, 2010, 2015,2020) was used to investigate the drought event and its situation in Qazvin plain, the central plateau of Iran. Drought characteristics are evaluated using the 24- month standardized precipitation index (SPI). The results of SPI indicated that insufficient precipitation, excessive use of groundwater for irrigation, and utilization of uncontrolled wells caused a significant reduction in groundwater aquifers from 2015 to 2020. To assess the performance of the SPI, a five-year moving average of available precipitation data was calculated, and the result confirmed the outcomes of SPI. Appropriate geostatistical interpolation methods are used to generate maps of drought zoning. Based on the results of this investigation in the northeastern part of the study area, June and November had the highest and the lowest rate of drought, respectively. The linear regression between the annual average of precipitation and the changes of groundwater aquifer level exposed a significant correlation of R² = 0.4253. Furthermore, linear regression between 24-month SPI and groundwater aquifer level indicated a correlation of R² = 0.614. Considering the results of this study, the reduction of groundwater aquifer levels in Qazvin plain from 2015 to 2015 exposed a significant negative difference compared to previous years (2005 to 2010). [ABSTRACT FROM AUTHOR]

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geo-statistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases. • The spatial distribution of mortality is less clustered than that of contagion. • Despite immunization, periods of high contagion meant a subsequent increase in mortality. • COVID-19 mortality in nursing homes presents a temporal concentration of deaths. • Elderly male residents in nursing homes were especially vulnerable. • Consecutive hot spots accounted for 51.45% of COVID-19 deaths. [ABSTRACT FROM AUTHOR]

The photogrammetry techniques are known to be accessible due to its low cost, while the geometric accuracy is a key point to ensure that models obtained from photogrammetry are a feasible solution. This work evaluated the discrepancies in 3D (DSM) and 2D (orthomosaic) models obtained from photogrammetry using control points (GCPs) near a reflective/refractive area (water body), where the objective was to evaluate these points, analysing the independence, normality and randomness and other basic statistic. The images were obtained with a 16 MP Canon PowerShot ELPH 110S with a modified NiR band and a multispectral sensor Parrot Sequoia, both embedded in a hex-rotor UAV in flight over the Unisinos University’s artificial lake in the city of São Leopoldo, Rio Grande do Sul, Brazil. Due the distribution of the data found to be not normal, we applied non-parametric tests Chebyshev’s Theorem and the Mann–Whitney’s U test, where it showed that the values obtained from Sequoia DSM presented significant similarities with the values obtained from the GCP’s considering the confidence level of 95%; however, this was not confirmed for the model generated from a Canon camera, showing that we found better results using the multispectral Parrot Sequoia.

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Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent studies have indicated that certain atmospheric pollutants had significantly reduced in several countries during the lockdown period imposed to curb the spread of SARS-CoV-2-Virus. The Government of India declared the first lockdown from the end of March 2020, which continued till June 2020 in most Indian states. The present study compares the air quality indicators nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) across India, during the months of March–August 2020 and the same period in 2019. The application of satellite information from NASA – Ozone Monitoring Instrument and Atmospheric Infrared Sounder were used to compare the quantum of air pollutants. The temporal variation of the air pollutants was studied using satellite imagery and geo-statistics on a monthly, national average basis, to assess the overall impact of the lockdown. NO2, SO2, and O3 showed some level of reduction during the period of study in 2020 when compared to 2019, whereas CO levels had gone up in 2020. NO2, a pollutant mainly arising from motor vehicle combustion, reduced by 3.98–12.1% in 2020 as compared to the same study period in 2019 and in April 2020, when there was a complete lockdown, it had dropped maximally (by 12.1%). The reduction in SO2 levels in 2020 ranged from around 0.5–9% but only during April–June 2020, whereas there was an increase in March, July, and August 2020 when compared to 2019. Despite a reduction in NO2, the O3 levels (which are dependent on NO2 levels) saw an increase in the atmosphere during March–May 2020 by 1.9–5%, and decreased during June–August 2020. The CO levels in the atmosphere did not reduce during lockdown; instead, it peaked in March, April, and May 2020, when compared to 2019, possibly due to incomplete combustion of materials containing carbon materials like wood, plastics, etc. This study demonstrates that it is possible to rapidly reduce atmospheric pollution in India. However, since the level of certain pollutants like O3 are dependent on others like NO2, reducing the atmospheric pollution globally is a sustained and concerted effort by all concerned. [ABSTRACT FROM AUTHOR]

This paper provides a summary of the percolation method of cluster analysis, and then describes two detailed archaeological case studies where the technique is applied at significantly different geographical scales. This demonstrates not only the potential for the technique within archaeology, but also how it can be applied at different spatial scales with different objectives appropriate to the specific problem in question. The technique, originally developed in physics and more recently adopted in geography, is a way of identifying groupings or clusters, purely based on spatial separation using Euclidian distance. The paper includes a discussion comparing percolation analysis with other clustering techniques already established within archaeology. The first case study uses percolation analysis as an exploratory tool for investigating the distribution of hillforts in Britain, identifying clusters and groupings at a national scale. The purpose is to identify possible socio-political entities for further investigation. In the second case study, the technique is applied to excavated features at a sub-regional level, in Saxony-Anhalt, central Germany, with the different objective of identifying settlement sites along a 13 km strip excavation. The aim here was to arrive at estimates of settlement sizes, which in turn can inform landscape archaeological surveys for deciding on the attribution of single finds to an already known site or registering a new site. Percolation analysis is shown to be an effective tool for analysing archaeological data sets at widely different geographical scales and should become a standard part of the archaeologist’s spatial analysis toolkit.

Soil pH is one of the chemical properties influencing the nutrient bioavailability. Most of the Pakistani soils are calcareous in nature. Keeping in view the limitations of classical statistics for explaining spatial heterogeneity a survey in the farmer grown rice fields was conducted for mapping of soil pH down the soil profile up to the depth of 100 cm using geo-statistics and GIS as a diagnostic tool. One hundred and seventy five soil samples were collected from the representative soil series of rice producing district Sheikhupura. Sampling was done from five depths of 0- 12, 12-24, 24-36, 36-60 and 60-100 cm. Soil profiles were excavated and samples were collected from the five depths to examine the variation of pH down the soil profile. Results indicated that soil pH at different depths was 7.80 ±0.45, 8.09 ± 0.42, 8.29±0.50, 8.39±0.54 and 8.47 ± 0.57 at 0-12, 12-24, 24-36, 36-60 and 60-100 cm depth, respectively. When geo-statistical analyses of the data were performed soil pH at all depths was found moderately to strongly spatial dependent (Nugget sill ratio

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).

Almost all causative factors of diseases depend on location. The Digital Earth approach is suitable for studying diseases globally. Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors. Through modelling, the most important causative factors can be extracted and the epidemiology of the disease can be observed. In this paper, skin cancer (the most common type of cancer) has been modelled based on its causative factors, including climate factors, people's occupations, nutrition habits, socio-economic factors, and usage of chemical fertiliser. To fit the model, a data framework was first designed, and then data were gathered and processed. Finally, the disease was modelled using Generalised Linear Models (GLM), a statistical model based on the location of the factors. The results of this study identify the most important causative factors together with their relative priority. Furthermore, a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors. This work illustrates the ability of the model to predict disease occurrence. Thus, by using this Digital Earth approach, skincancer can be studied in all the key countries around the world. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

So that one may obtain the economic value of an environmental resource, its monetary value should be estimated in relation to other goods/services available in the economy. Environmental valorization methods currently used are relatively new and the variables in the process may not be known or partially known. One method is the Reposition Cost that calculates the value of reparation from a damage. Current paper applied the reposition cost method in the economic valorization of nutrient loss by soil erosion in the hydrographic basin of the river Una, Brazil, through geoprocessing to evaluate spatially the area under analysis. Thirty-five samples were collected and the soil´s nutrients quantified, Ca, Mg, P and K. Concentrations were spatialized and annual loss estimated, with reposition costs. In more than 89% of the studied area, the reposition cost of K, P, Ca and Mg of the three fertilizers employed (potassium chloride, potassium chlorate, simple superphosphate and lime) reached about R$ 0.01 per m². Total reposition cost was R$ 352,846.43. The spatialization of reposition costs showed which areas required special attention of the government with regard to monitoring and proposition of sustainability. [ABSTRACT FROM AUTHOR]

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Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)