Geostatistical Analysis Research Articles

Geostatistical Analysis of the Spatial Variation of Chrysolina aeruginosa Larvae at Different Stages in Desert Ecosystems.

Chrysolina aeruginosa is a major pest of Artemisia ordosica, and knowledge of the spatial distribution pattern of its larvae in their natural habitat is crucial for the implementation of effective control measures. This study employed geostatistical methods to investigate the damage caused by larvae of different age groups and their spatial distribution pattern. The distribution of C. aeruginosa larvae, which cause damage to A. ordosica, differed significantly according to their age. Younger larvae were predominantly found in the middle and upper parts of the plant, whereas older larvae were mainly distributed in the middle and lower parts, with significant differences in distribution location. A generalized linear model analysis revealed that the height of the plant, and plant morphological characteristics such as height, crown width, and ground diameter were significantly correlated with the number of larvae present. Furthermore, the interaction of age with other variables had an impact on the number of larvae. Kriging interpolation showed that C. aeruginosa larvae were distributed in aggregated patches with strong spatial heterogeneity. The younger larvae were more abundant in the center of the sample site, while the older larvae tended to be distributed toward the edges. These findings provide valuable information for designing effective control programs.

Determining the Extent of Soil Degradation Processes Using Trend Analyses at a Regional Multispectral Scale

In order to ensure the sustainability of production from agricultural lands, the degradation processes surrounding the fertile land environment must be monitored. Human-induced risk and status of soil degradation (SD) were assessed in the Northern-Eastern part of the Nile delta using trend analyses for years 2013 to 2023. SD hotspot areas were identified using time-series analysis of satellite-derived indices as a small fraction of the difference between the observed indices and the geostatistical analyses projected from the soil data. The method operated on the assumption that the negative trend of photosynthetic capacity of plants is an indicator of SD independently of climate variability. Combinations of soil, water, and vegetation’s indices were integrated to achieve the goals of the study. Thirteen soil profiles were dug in the hotspots areas. The soil was affected by salinity and alkalinity risks ranging from slight to strong, while compaction and waterlogging ranged from slight to moderate. According to the GIS-model results, 30% of the soils were subject to slight degradation threats, 50% were subject to strong risks, and 20% were subject to moderate risks. The primary human-caused sources of SD are excessive irrigation, poor conservation practices, improper utilisation of heavy machines, and insufficient drainage. Electrical conductivity (EC), exchangeable soil percentage (ESP), bulk density (BD), and water table depth were the main causes of SD in the area. Generally, chemical degradation risks were low, while physical risks were very high in the area. Trend analyses of remote sensing indices (RSI) proved to be effective and accurate tools to monitor environmental dynamic changes. Principal components analyses were used to compare and prioritise among the used RSI. RSI pixel-wise residual trend indicated SD areas were related to soil data. The spatial and temporal trends of the indices in the region followed the patterns of drought, salinity, soil moisture, and the difficulties in separating the impacts of drought and submerged on SD on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification should proceed using indices as a factor predictor of SD analysis.

Stochastic analysis of excavation-induced wall deflection and box culvert settlement considering spatial variability of soil stiffness

In this study, a three-dimensional (3D) finite element modelling (FEM) analysis is carried out to investigate the effects of soil spatial variability on the response of retaining walls and an adjacent box culvert due to a braced excavation. The spatial variability of soil stiffness is modelled using a variogram and calibrated by high-quality experimental data. Multiple random field samples (RFSs) of soil stiffness are generated using geostatistical analysis and mapped onto a finite element mesh for stochastic analysis of excavation-induced structural responses by Monte Carlo simulation. It is found that the spatial variability of soil stiffness can be described by an exponential variogram, and the associated vertical correlation length is varied from 1.3 m to 1.6 m. It also reveals that the spatial variability of soil stiffness has a significant effect on the variations of retaining wall deflections and box culvert settlements. The ignorance of spatial variability in 3D FEM can result in an underestimation of lateral wall deflections and culvert settlements. Thus, the stochastic structural responses obtained from the 3D analysis could serve as an effective aid for probabilistic design and analysis of excavations.

Integrated approach for the investigation of groundwater quality using hydrochemical and geostatistical analyses in Wadi Fatimah, western Saudi Arabia

The Wadi Fatimah area suffers from a lack of freshwater resources, so this study aimed to analyze the groundwater in this region and evaluate its quality for irrigation and drinking purposes. Eleven water quality parameters, including pH, total dissolved solids (TDS), Ca2+, HCO−3, NO−3, F−, Cl−, K+, Mg2+, SO2−4, and Na+, were utilized to evaluate the quality of the water and produce a water quality index (WQI). These parameters were measured at 100 different chosen locations. The spatial distribution map revealed that all parameters are high in the southern part except K+. Using the correlation matrix, a high positive correlation is obtained among TDS, Cl−, Mg2+, and Ca2+ in addition to a high correlation among TDS with Cl− and Na+. From PCA analysis, PCA1, PCA2, and PCA3 represent about 52%, 12%, and 10% of all components along the study area, respectively. PCA1 has low variance than PCA2 and PCA3. The majority of the Southern region’s sites went from having extremely poor to poor water classifications and from poor to unsuitable water. However, the center part possesses exceptionally high-quality groundwater. According to the results from the current study’s water quality index, the presence of nitrate and fluoride in the groundwater samples was primarily responsible for their high WQI values. The statistics showed that a higher percentage of the population had poor drinking water due to direct pollutant release, agricultural effects, and excessive groundwater resource use. The study offers a groundwater quality modeling technique that is both affordable and replicable in other areas.

Spatial Variability of Soil Erodibility at the Rhirane Catchment Using Geostatistical Analysis

Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary Kriging interpolation maps. Results showed that mean values of soil organic matter content were statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The analysis of variance of the organic matter provided a tool for identifying significant differences when comparing means between the soil types. The soil granulometry is mainly composed of silt and fine sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034, which was greater in soils with calcic horizons. Statistical evaluation by using Pearson’s correlation revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand (−0.16%), clay (−0.56%), organic matter (−0.32%), permeability (−0.41%), soil structure (−0.40%), and the soil stability index (−0.26%). The variability analysis of the K-factor showed moderate spatial dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland and sparse grassland land uses. Overall, the study provides scientific support for soil conservation management and appropriate agricultural food practices for food supply.

Trace element contamination in soils surrounding the open-cast coal mines of eastern Raniganj basin, India.

Trace element pollution of soils surrounding coal-mining areas affects the health of local communities. The increasing coal-mining and associated activities in the Raniganj basin (east India) have led to increased soil concentration of certain trace elements. To quantify the elevated trace element (TE) concentrations in the soil surrounding coal-mining areas, 83 surface soil, coal, and shale samples were collected from open-cast mining areas of the eastern Raniganj basin. The soils present are sandy silt, silty sand, and silty in nature, but almost no clay. They are acidic (pH = 4.3) to slightly alkaline (pH = 7.9) with a mean electrical conductivity (EC) of 340.45µS/cm and a mean total organic carbon (TOC) of 1.80%. The northern and western parts of the study area were found to be highly polluted by certain metallic trace elements. The relevant environmental indices, geoaccumulation index (Igeo), contamination factors (CF), enrichment factors (EF), and pollution load index (PLI) were calculated and assessed. Analysis revealed that Cr was highly enriched in these soil samples, followed by Pb, Co, Cu, Cd, Fe, Ni, Mn, Zn, As, and Al. Geostatistical analyses (correlation coefficients and principal component analysis) indicated that the occurrence of some trace elements (Al, Cd, Co, Cu, Fe, Mn, Ni, and Zn) is most likely linked to the various coal-mining operations in the study area. However, the anomalous Cr and Pb distributions are likely influenced by other anthropogenic, mainly industrial, inputs besides coal mining. These results justify the adoption of rigorous soil monitoring programs in the vicinity of coal-mining areas, to identify pollution hotspots and to develop strategies to reduce or mitigate such environmentally damaging pollution.

From settlement patterns to memory of place among Holocene hunter-gatherers at Sai Island, Middle Nile Valley

ABSTRACT Persistent places are the locations where people aggregate, utilise and reuse natural or built features and develop their social identities and interactions. A network of persistent places forms interconnected persistent settlement patterns, which create a humanly made or storied landscape with a shared community-based memory of place. Although it has been demonstrated that persistent settlement patterns long preceded the onset of Neolithic farming villages, the Levant has received the most attention regarding this perspective. At the same time, Africa still needs to be addressed. This paper provides an illustrative case study of persistent settlement patterns created by Holocene hunter-gatherers in the Middle Nile Valley of Sudan. It employs geostatistical patterning and visual mapping of an extensive collection of different classes of artefacts (lithic industry, hammerstones, ground stone tools and pottery) from a well-preserved Khartoum Variant site on Sai Island and correlates them to complex architectural features exposed on an archaeological surface (Level 1) at site 8-B-10C dating to the early fifth millennium cal. BC. The combination of multiple in-depth geostatistical analyses of a complex habitation system successfully documents an exceptionally preserved planned intra-site settlement organisation maintained over several generations, revealing a memory of place.

Preliminary Site Investigation based on RGB Electromagnetic Energy of Landsat-7 Images in Wadi Fayidah, Saudi Arabia

In this paper, geostatistical analysis of digital image processing data efficiently contributed to the preliminary site investigation and geotechnical mapping of Wadi Fayidah, Saudi Arabia. 3D modeling, clustering, and chart pattern changes were used to analyze the spectral electromagnetic energy reflected values in red, green, and blue (RGB) ranges on false color composite Landsat-7 images. Therefore, from upstream to downstream, a series of measurements were carried out on a 70km dendritic drainage pattern at 78 stations. Wadi Fayidah was found to have a dominant structural lineament of 56° to 84°. Furthermore, as a preliminary engineering geology mapping, Wadi Fayidah has 9 lithofacies that may differ in engineering geological properties.

Integration of fractal modeling and correspondence analysis reconnaissance for geochemically high-potential promising areas, NE Iran

Most geochemical and geostatistical analysis in mining exploration requires removing regional trends in order to obtain local anomalies. In this paper, stream sediment samples, which collected from Khusf area (NE Iran), was studied based on Concentration-Area (C-A) fractal model as well as correspondence analysis methods to find high-potential areas elements. Correspondence analysis with 170 samples through 20 elements concentration values in each sample was performed. According to correspondence analysis, among one or several elements in the study area, local anomalies were separated which the highest concentration relates to the variables Pb, As, and Cd elements. After the correspondence analysis, the best variogram for the Khusf area was studied. Elemental concentration maps was then produced through estimating the values using kriging method. Therefore, using the fractal method, between three statistical sets of elements, it was concluded that the third set showed the anomaly for Pb, As, and Cd elements with local anomaly values respectively determined as 55, 7.2, and 0.88 ppm. Although Cd element is not genetically related to same source of Pb and As, utilizing integrated approach, Cd anomalies has also been detected in the area as a promising element zone. Finally, it is suggested that this region has the possibility of Basic Metals occurrence and suggests further geophysical operations on a local identification scale.

Spatial heterogeneity of soil available medium- and micro-elements in evergreen-deciduous broadleaved forest in karst.

Understanding the spatial heterogeneity of soil available medium- and micro-elements in karst area can provide a valuable theoretical guideline for soil nutrient management of karst ecosystem. We collected soil samples at a soil depth of 0-10 cm using grid sampling (20 m×20 m) in a 25 hm2 (500 m×500 m) dynamic monitoring plot. We further analyzed the spatial variability of soil medium- and micro-elements and their drivers, with classic statistics analysis and geo-statistics analysis. The results showed that the average contents of exchangeable Ca and Mg and available Fe, Mn, Cu, Zn, and B were 7870, 1490, 30.24, 149.12, 1.77, 13.54, and 0.65 mg·kg-1, respectively. The coefficient of variation of the nutrients ranged from 34.5% to 68.8%, showing a medium degree of their spatial variation. The coefficient of determination of the best-fit semi-variogram models of each nutrient was higher than 0.90, except for available Zn (0.78), indicating a strong predictive power for the spatial variation of the nutrients. The nugget coefficients for all the nutrients were less than 50%, showing a moderate spatial correlation, and the structural factors played a pivotal role. The spatially autocorrelated variation was within the range of 60.3-485.1 m, among which available Zn showed the lowest range and the deepest fragmentation degree. The spatial distribution of exchangeable Ca, Mg, and available B were consistent, with contents in the depression being significantly lower than that in other habitats. The contents of available Fe, Mn, and Cu declined with the increases of altitude and were significantly lower on the hilltop than in other habitats. The spatial variation of soil medium- and micro-elements was closely related to topographic factors in karst forest. Elevation, slope, soil thickness, and rock exposure rate were the primary drivers of spatial variation of soil elements and need to be considered in soil nutrient management of karst forestlands.

GEOSPATIAL DISTRIBUTION OF FOREST SPECIES IN THE BOTANICAL GARDEN OF RECIFE, PERNAMBUCO, BRAZIL

The adoption of appropriate strategies for the conservation and management of forest ecosystems associated with the Atlantic Forest depends on understanding the pattern of spatial distribution of forest species, mainly in protected areas. Thus, this study aimed to evaluate the geospatial distribution of forest species in a fragment of Atlantic Forest (Lowland Dense Ombrophilous Forest) in the Botanical Garden of Recife, Pernambuco, Brazil. The phytosociological study was carried out by the plot method, with 40 plots of 10 m x 25 m, systematically distributed, where all living individuals that showed circumference at 1.3 m above the ground ≥ 15 cm were identified and georeferenced. To identify the existence of floristic similarity between the plots, the obtained data were subjected to hierarchical cluster analysis. The spatial variability of forest species was characterized using geostatistical analysis. The data were interpolated by kriging, followed by the construction of thematic maps of spatial distribution. Hierarchical cluster analysis revealed the formation of two groups of plots. The forest species Artocarpus heterophyllus, Dialium guianense, Helicostylis tomentosa, Hevea brasiliensis, Pouteria durlandii and Thyrsordium spruceanum showed spatial dependence. The use of geostatistics allowed visualizing the spatial distribution arrangements and identifying the concentration sites of these species. Information on the geospatial distribution of forest species can be used for the preservation, conservation and management of forest remnants.

Geo-Statistical Analysis of Meteorological Drought and Recurrence Intervals in the Context of Climate Change Over Extreme Northeastern Region of Nigeria

This study examined the occurrence of meteorological drought in the context of climate change over the extreme northeastern region of Nigeria using rainfall and temperature data spanning a period of 60 years (1961-2020) and 40 years (1981-2020), respectively. Linear regression, 5-year moving average and Cramer’s test were used to examine the changes in the rainfall and temperature. Rainfall Anomaly Index was used in depicting periods of different drought intensities in the region. The trend analysis of the climate data revealed that the region has been experiencing increasing rainfall and temperature in recent years. Findings also revealed that the droughts of the 1980s were more severe than the Great Sahelian Droughts of 1968-1973. The year 1987 was the driest in the whole period of study (1961–2020). Contrariwise, about 81% of the region from 2000 to 2020 experienced normal to very wet conditions. The mean probabilities of mild, moderate, and severe droughts in the area were 0.14 (recurrence interval of 8 years), 0.11 (recurrence interval of 11 years), and 0.07 (recurrence interval of 16 years), respectively. This study concludes that the number of drought occurrences has decreased in recent years. Therefore, government policies related to agriculture and water resources development in the area should take into account the increasing rainfall and temperature in recent years.
 Keywords: Climate Change, Meteorological drought, Rainfall, Recurrence Interval, Severe Drought, Temperature

Spatial distribution and accumulation of arsenic in biological samples and associated health risks by drinking groundwater in Bahawalnagar, Pakistan

Groundwater contamination by arsenic (As) is a major health/environmental concern due to its high toxicity to humans. Despite substantial research, there is scarce data in many countries regarding spatial distribution and As levels in groundwater, as well as its possible accumulation in humans/animals and associated health hazards. In this study, a total of 124 drinking water, 20 animal milk, 21 human hair, and 14 human nail samples were collected and analyzed for As contents from four tehsils (Bahawalnagar, Chishtian, Haroonabad and Minchinabad) of District Bahawalnagar. Geostatistical analysis and risk assessment models were used to predict the spatial distributions and health hazards of As in drinking water. Arsenic groundwater contents ranged between below detection limit (BDL) to 31.5 μg.L−1 with a mean of 7.6 μg.L−1. About 6 water samples (5%) contained As level > World Health Organization (WHO) limit. All these samples containing As contents > WHO limit were collected from Chishtian tehsil. In the case of milk samples, mean As contents were 75 μg.L−1 with a range of 42–111 μg.L−1. Arsenic was also detected in human hair (mean value of 640 μg kg−1 and range 71–2139 μg kg−1). However, As contents were BDL for all the nail samples. Risk assessment indices (cancer risk and hazard quotient) revealed possible health hazards due to their high values (3.0 and 0.0013, respectively). The principal component analysis (PCA) revealed that the potential sources of As release in the groundwater of study area do not match with those of other water variables. Hence, there is a need to monitor the groundwater quality of the Chishtian tehsil for any potential negative health impacts, as well as the possible sources of As in the groundwater of the study area.

“15-Minute City” and Elderly People: Thinking about Healthy Cities

Considering the global scenario of population aging, which countries such as Chile are going through, the social problems that it means in terms of viability and quality of life for the elderly are increasing and are a cause for concern. For this reason, this study summarizes the results of investigating the accessibility of services and recreational spaces under the parameters of a “15-minute city” for the elderly people in the city of Santiago de Chile. The investigation employed a multivariate geostatistical analysis with a quantitative approach and was developed on a census block scale to test some of the principles of the 15-min city along with the principles on active aging of the elderly. The results are surprising, show a good territorial coverage for the study area and open the possibility of Santiago becoming a 15-min city for older adults. However, there are still several challenges in terms of public policies, from mental and physical health to the design of public spaces, which are fundamental to think about for cities of the future.

Assessing spatial variability of soil and drawing location-specific management zones for coastal saline soils in Ramanathapuram District, Tamil Nadu

The production of crops in saline and alkali-degraded areas is difficult due to the heterogeneous and spatial variation of soil fertility. First, their spatial variability was analyzed and maps of the spatial distribution were created using Geostatistical techniques. The fuzzy k-mean clustering analysis was then used to define Management zones in the coastal saline soils of Ramanathapuram district in Tamil Nadu. One hundred and fifty geo-referenced soil samples (30 cm depth) were taken and analyzed for pH, electrical conductivity (ECe) in the saturated paste extract (USSL method), organic carbon (OC) (Walkley-Black chromic acid wet oxidation method), calcium carbonate (CaCO3) (Rapid titration method) and available phosphorus and extractable micronutrients (Multinutrients extraction method), revealing significant variation in soil characteristics throughout the coastal saline soils of Ramanathapuram district. The most significant factors, which together accounted for four principal components and 69% of the overall variability, were pH, electrical conductivity (ECe), calcium Carbonate and available zinc. According to Geostatistical analysis, the Exponential (pH, OC (organic carbon), P, Fe, Mn and Zn) and Stable (ECe) was the best fit semivariogram ordinary kriging model with weak to moderate spatial dependence. Fuzzy k-mean clustering was also used to identify zone 1, zone 2 and zone 3. For every soil property, there was a significant difference between MZ1(zone 1), MZ2(zone 2) and MZ3(zone 3). These results also showed that cluster analysis gave farmers a chance to use location-specific nutrient management strategies by minimizing variability within the zone. The management zones can decrease agricultural inputs and environmental pollutants while increasing crop productivity.

A Geo-Statistical Analysis of the Impact of Ecological and Environmental Risks on Epidemiology in the South-west, Nigeria

Introduction: The probability of contamination is frequently elevated in scenarios where a well and pit latrine coexist, or in situations where heavy rain causes the overflow of open excreta dumps, which in turn flush into wells and surface water. Many possible negative health effects might arise from exposure to various ecological and biological agents in the environment. Therefore, there is a need to examine the risk of disease transmission in Ife North Local Government Area (LGA) of Osun state, using epidemiological, environmental, and ecological factors.
 Materials and Methods: Geostatistical analysis was used to examine the epidemiological risk, based on various environmental, biological, and ecological variables. The technique employed demonstrated the complexity and multiple parameters that raise the probability of an epidemic. The Shapiro-Wilk test was used to determine whether or not the data were normally distributed. Fuzzy logic, correlation, and spline surface interpolation analysis were conducted using ArcGIS 10.3 and ENVI 5.0 software. Three levels of epidemic risk were used to construct the disease surveillance and projection maps.
 Results: According to the final susceptibility map, 8.08 km2 of 460.12 km2 of the research area were considered to be at very low risk for an epidemic, followed by 364.98km2 of low risk and 87.06km2 of moderate risk areas, with percentages of 1.75%, 79.32%, and 18.92%, respectively.
 Conclusion: A very substantial correlation was observed between biological and ecological components and water-borne diseases. It is, therefore, advised that all water sources be treated before consumption, and community involvement be encouraged in environmental sanitation programs.

Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms

Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate. For prediction, we used a set of fifteen causative factors as a cross-function of soil, site and stand-related predictors. A total of 80 sample plots, including 1134 affected trees, were surveyed and used for the modeling process at the 5600-ha landscape level of the southern Zagros, Iran, where the disease occurs in roughly 25% of forest lands. Ten machine learning algorithms were explored and the performance of each algorithm to predict oak disease outbreak was evaluated. The modeling framework used maximum entropy to remove the least influential variables and build the status-quo management scenario to which the results of the prediction models were compared. Results showed that the random forests algorithm (AUC = 0.96: Precision = 0.71: Accuracy = 0.90: F-Measure = 0.70) achieved significantly better results than the status-quo management (Precision = 0.13: Accuracy = 0.67: F-Measure = 0.12) and any other algorithm. Soil chemical properties (NPK, organic carbon and EC) and landform predictors (slope, distance to roads, and TWI) were major forecasters of oak disease outbreak identified by the random forest algorithm. Geostatistical analysis enabled the creation of a map that identified sites at higher risk of infestation, allowing epidemiologists and forest managers to find sites likely to be infested. Consequently, financial resources can be allocated and management practices such as sanitation felling treatments applied across large forest landscapes to minimize the risk of spread and severity to uninfested high-value trees on nearby or adjacent land zones that are in the early stage of epidemics.

Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.

Positive Matrix Factorization as Source Apportionment of Paddy Soil Heavy Metals in Black Shale Areas in Western Zhejiang Province, China

The source apportionment of pollutants is the key to preventing and controlling the pollution caused by heavy metals in soils. The aim of this study was to investigate the main sources of heavy metals in the soils of black shale areas in western Zhejiang, China. Based on geostatistical spatial analysis, this research employed positive matrix factorization (PMF) for the source apportionment of heavy metals in paddy soil. The results showed that contaminated arable soils were concentrated in the western and southern study areas. At least five major sources of heavy metals were screened in this study: natural sources (39.66%), traffic emissions (32.85%), industrial emissions (9.23%), agricultural activities (9.17%), and mining (9.10%). To be specific, Cd was mainly from mining; As originated from agricultural inputs such as fertilizers and pesticides; and Hg, as an industrial pollutant, was transported by atmospheric deposition in the study area. The accumulation of Pb, Zn, and Cu was mainly influenced by natural sources and anthropogenic sources, i.e., traffic emissions, while that of Cr and Ni was controlled by natural sources.

Geostatistical analysis from the clinical laboratory in cardiovascular prevention for primary care

Introduction and objectivesCardiovascular diseases continue to lead the ranking of mortality in Spain. The implementation of geostatistical analysis techniques in the clinical laboratory are innovative tools that allow the design of new strategies in primary prevention of cardiovascular disease. The aim of this study was to study the prevalence and geolocation of severe dyslipidemia in the health areas under study in order to implement prevention strategies in primary care. A retrospective cohort study of low-density protein-bound cholesterol, triglyceride and lipoprotein (a) levels in the years 2019 and 2020 were carried out. In addition, a geostatistical analysis was performed including representation in choropleth maps and the detection of clustering clusters, using geographic information in zip code format included in the demographic data of each analytic. ResultsThe analytical data included in the study were triglycerides (n=365,384), low density protein-bound cholesterol (n=289,594) and lipoprotein to lipoprotein (a) (n=502). Areas with the highest and lowest percentage of cases were identified for the established cut-off points of LDL-C>190mg/dl and TG>150mg/dl. Two clustering clusters with statistical significance were detected for cLDL>190mg/dl and a total of 6 clusters for TG values>150mg/dl. ConclusionsThe detection of clusters, as well as the representation of choropleth maps, can be of great help in detecting geographic areas that require greater attention to intervene and improve cardiovascular risk.