Spatial distribution of soil organic carbon quality descriptors determining factors that affect its sequestration in Northeast Algeria.

Increasing soil organic carbon (SOC) content is crucial for soil fertility, conservation, and combating climate-related issues by sequestering CO2. While existing studies explore the total content of SOC, few of them investigate the factors that favor its sequestration and the impact of land use type and management. This research aims to study the spatial variation of the total content and the quality or maturity (in terms of aromaticity) of the humic acid (HA) fraction, along with the factors that enhance its formation and conservation for a longer time in the soil. In addition, the study tries to evaluate the performance of the Regression Kriging (RK) method in producing interpolation maps that describe the natural variation of the SOC and its quality with the aim of defining and preventing soil degradation. Finally, the study aims to evaluate the impact of the land use type and the importance of dense vegetation in the sequestration of the organic carbon (OC) in the soil. The analysis of the SOC was performed in northeast Algeria's semi-arid climate, examining content, quality, and chemical composition. Using geostatistical methods (RK), SOC is correlated with most related factors, producing detailed interpolation maps. The results showed that the SOC and its HA fraction (both its total content and its degree of transformation or maturity (measured in terms of aromaticity and structural condensation) are highly correlated to the topography of the area (P < 0.05). Results reveal variations in HAs' composition across land covers. Notably, areas subjected to burning exhibited a 21% increase in HA aromaticity compared to forested regions and a 29% increase relative to cultivated areas. The study highlights that soil cover has a substantial influence on the performance of SOC sequestration, the forested areas have a positive impact on the storage of SOC in the form of HA with a more complex chemical composition that suggests increased aromaticity and resilience. As a whole, the results indicate the potential of geostatistical methods to provide valuable information about the factors that influence the current status and evolution of SOC in the study area.

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach.

Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets-ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables.

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data.

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 µg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Detecting high-risk neighborhoods and socioeconomic determinants for common oral diseases in Germany.

BACKGROUND: Ideally, health services and interventions to improve dental health should be tailored to local target populations. But this is not the standard. Little is known about risk clusters in dental health care and their evaluation based on small-scale, spatial data, particularly among under-represented groups in health surveys. Our study aims to investigate the incidence rates of major oral diseases among privately insured and self-paying individuals in Germany, explore the spatial clustering of these diseases, and evaluate the influence of social determinants on oral disease risk clusters using advanced data analysis techniques, i.e. machine learning. METHODS: A retrospective cohort study was performed to calculate the age- and sex-standardized incidence rate of oral diseases in a study population of privately insured and self-pay patients in Germany who received dental treatment between 2016 and 2021. This was based on anonymized claims data from BFS health finance, Bertelsmann, Dortmund, Germany. The disease history of individuals was recorded and aggregated at the ZIP code 5 level (n = 8871). RESULTS: Statistically significant, spatially compact clusters and relative risks (RR) of incidence rates were identified. By linking disease and socioeconomic databases on the ZIP-5 level, local risk models for each disease were estimated based on spatial-neighborhood variables using different machine learning models. We found that dental diseases were spatially clustered among privately insured and self-payer patients in Germany. Incidence rates within clusters were significantly elevated compared to incidence rates outside clusters. The relative risks (RR) for a new dental disease in primary risk clusters were min = 1.3 (irreversible pulpitis; 95%-CI = 1.3-1.3) and max = 2.7 (periodontitis; 95%-CI = 2.6-2.8), depending on the disease. Despite some similarity in the importance of variables from machine learning models across different clusters, each cluster is unique and must be treated as such when addressing oral public health threats. CONCLUSIONS: Our study analyzed the incidence of major oral diseases in Germany and employed spatial methods to identify and characterize high-risk clusters for targeted interventions. We found that private claims data, combined with a network-based, data-driven approach, can effectively pinpoint areas and factors relevant to oral healthcare, including socioeconomic determinants like income and occupational status. The methodology presented here enables the identification of disease clusters of greatest demand, which would allow implementing more targeted approaches and improve access to quality care where they can have the most impact.

Temporal trends and spatial and spatiotemporal distribution of schistosomiasis mansoni in northeast Brazil between 2005 and 2016.

BACKGROUND: Schistosoma mansoni is a parasitic disease of great magnitude for Brazilian public health. We aimed to analyse the temporal trend and spatial and spatiotemporal distribution of positivity rates for schistosomiasis mansoni in northeast Brazil. METHODS: This is a descriptive study with an ecological approach, carried out between 2005 and 2016. We calculated the positivity rate for the disease and then performed a segmented trend analysis (Joinpoint). For spatial analysis, we smoothed the positivity rates using the local empirical Bayesian method. We checked for spatial autocorrelation using Moran's global and local. Subsequently, we performed Kulldorff's space time sweep analysis. RESULTS: In the period under review, 7 745 650 tests were performed in the northeast, of which 577 793 were positive for Schistosoma mansoni. In the historical series of positivities, it is noted that the highest rates were in Sergipe, Alagoas and Pernambuco. The states of Alagoas and Sergipe showed higher positivity in relation to the average positivity of the northeast and of Brazil. The spatial analysis maps identify clusters of high risk of schistosomiasis cases, mainly in coastal municipalities. There was also stability in positivity rates in some states and the maintenance of endemic areas. CONCLUSIONS: Thus effective public health policies are needed in health education in order to reduce schistosomiasis positivity and improve the health conditions of the northeastern population.

Mapping heterogeneity in family planning indicators in Burkina Faso, Kenya, and Nigeria, 2000-2020.

BACKGROUND: Family planning is fundamental to women's reproductive health and is a basic human right. Global targets such as Sustainable Development Goal 3 (specifically, Target 3.7) have been established to promote universal access to sexual and reproductive healthcare services. Country-level estimates of contraceptive use and other family planning indicators are already available and are used for tracking progress towards these goals. However, there is likely heterogeneity in these indicators within countries, and more local estimates can provide crucial additional information about progress towards these goals in specific populations. In this analysis, we develop estimates of six family indicators at a local scale, and use these estimates to describe heterogeneity and spatial-temporal patterns in these indicators in Burkina Faso, Kenya, and Nigeria. METHODS: We used a Bayesian geostatistical modelling framework to analyse geo-located data on contraceptive use and family planning from 61 household surveys in Burkina Faso, Kenya, and Nigeria in order to generate subnational estimates of prevalence and associated uncertainty for six indicators from 2000 to 2020: contraceptive prevalence rate (CPR), modern contraceptive prevalence rate (mCPR), traditional contraceptive prevalence rate (tCPR), unmet need for modern methods of contraception, met need for family planning with modern methods, and intention to use contraception. For each country and indicator, we generated estimates at an approximately 5 × 5-km resolution and at the first and second administrative levels (regions and provinces in Burkina Faso; counties and sub-counties in Kenya; and states and local government areas in Nigeria). RESULTS: We found substantial variation among locations in Burkina Faso, Kenya, and Nigeria for each of the family planning indicators estimated. For example, estimated CPR in 2020 ranged from 13.2% (95% Uncertainty Interval, 8.0-20.0%) in Oudalan to 38.9% (30.1-48.6%) in Kadiogo among provinces in Burkina Faso; from 0.4% (0.0-1.9%) in Banissa to 76.3% (58.1-89.6%) in Makueni among sub-counties in Kenya; and from 0.9% (0.3-2.0%) in Yunusari to 31.8% (19.9-46.9%) in Somolu among local government areas in Nigeria. There were also considerable differences among locations in each country in the magnitude of change over time for any given indicator; however, in most cases, there was more consistency in the direction of that change: for example, CPR, mCPR, and met need for family planning with modern methods increased nationally in all three countries between 2000 and 2020, and similarly increased in all provinces of Burkina Faso, and in large majorities of sub-counties in Kenya and local government areas in Nigeria. CONCLUSIONS: Despite substantial increases in contraceptive use, too many women still have an unmet need for modern methods of contraception. Moreover, country-level estimates of family planning indicators obscure important differences among locations within the same country. The modelling approach described here enables estimating family planning indicators at a subnational level and could be readily adapted to estimate subnational trends in family planning indicators in other countries. These estimates provide a tool for better understanding local needs and informing continued efforts to ensure universal access to sexual and reproductive healthcare services.

Bayesian hierarchical modelling of the geospatial distribution of fasciolosis in dairy cattle and the impact on production: Application to the main milk-producing region (Galicia) in Spain.

Fasciolosis caused by Fasciola hepatica is a common parasitic infection among cattle in many countries. Although infected adult cows rarely show overt clinical signs, milk production may be impaired. Thus, significant production losses may occur in dairy herds with a high prevalence of fasciolosis. In this study, Bayesian hierarchical modelling was used to estimate the geospatial distribution of dairy cattle fasciolosis and its impact on milk production. The study was conducted in Galicia, the main milk producing region in Spain and a geographically heterogeneous area. The aims were: 1) to model the geospatial distribution of fasciolosis in dairy herds in the study area, 2) to identify clusters of herds with a high prevalence of fasciolosis, and 3) to assess the effect of fasciolosis on milk yield and quality. A large number of dairy cattle farms (n = 4907), of which 1660 provided production records, were surveyed. Fasciola infection status was determined by applying the MM3-SERO ELISA test to bulk tank milk samples. A high probability of infection was predicted in several zones, particularly in the centre, northeast and southeast of Galicia. Conversely, the predicted probability was very low in some parts of the northwest of the region. Infections with high within-herd prevalence (> 25% lactating cows infected) predominated. High within-herd prevalence was associated with loss of milk production (-1.387 kg/cow/ day, on average). No association between Fasciola infection and either milk fat or protein content was observed. This study has generated the first maps of the spatial distribution of the probability of Fasciola infection in dairy cattle herds in Galicia. The maps presented here can be used for reference purposes, enabling the design of better targeted fasciolosis control programmes in the region. Use of Bayesian hierarchical statistical analysis enabled us to ascertain the uncertainty of the predictions and to account for the spatial autocorrelation in the data. It also enabled us to generate maps showing the residual spatial variation in milk production, a topic that may deserve more detailed study.

The search for plausible economic mineral deposits in the central parts of Tanzania; insight from stream sediment geochemistry, multivariate statistics and geostatistics.

Exploration success relies heavily on the data obtained, but, significantly on the type of analytical methods deployed and the interpretation reached. A poorly analyzed data may obscure the true reflectivity of the data, and thus, compromised the decision made. A combined data processing approach of descriptive statistics, enrichment-depletion data normalization, geospatial elemental distribution, and stacked overlayed comparison of elements have been used in this study. The prime purpose was to demonstrate potential elemental anomalies, and predict areas of higher degree of confidence for subsequent exploration and mineral resource evaluation. One-hundred and sixty-six stream sediment samples from the Dodoma Region of the Tanzania Craton have been examined; to reveal potential elements or mineral commodity that warrant further exploration. Forty-three elements of target were examined, as this craton is globally known for its rich earth mineral commodity. Our result indicates an enrichment of transition metals (TMs) (Cu, Ni, Co, Cr, Mn and Zn), High Field Strength Elements (Y, Th, U, Zr, Nb, Hf, Ta and Pb), Large Ion Lithophile Elements (Ba and Rb) and Rare-Earth Elements (La and Ce), Platinum Group Element (Pd and Pt) and other metals (Au, As, Bi, W, Mo and Li). Obtained results point to a likely poly-metallic sources and processes; as the underlain geology is marked largely by pegmatite and migmatites, and moderate proportion of fine clastic sedimentary rocks, and minor volcanic rocks mostly to the northern domain. Theoretically, the Large Ion Lithophile Elements (LILEs), Rare-Earth Elements (REEs) and Platinum Group Elements (PGEs) are associated with felsic rocks or variable stages of plutonic granitization. Although, the TMs are often associated with mafic-ultramafic rocks, the linkage of such metals with organic-rich shales been reported elsewhere. These rocks may equally contribute to the occurrence of other metals as stated in this paper. Its intriguing to note a strong positive correlation of Li with TMs, possibility of Li control by mafic minerals in pegmatite bodies. This work proposes a polymetallic enrichment controlled by the area geology. To suggest an alluvial mining potential of the above elements in the area, resource evaluation is a requirement. The geospatial maps reveal areas worth focusing for subsequent exploration. The adopted geostatistical methods and other approach utilized in this research are effective, indicative of handling bulk exploration data for decision and subsequent exploration.

Spatial Patterns of Frangula alnus (Rosales: Rhamnaceae): Implications for Invasive Plant Management.

Glossy buckthorn (Frangula alnus) (Rosales: Rhamnaceae) is an invasive shrub from Europe that has been invading North America for over a century and threatening native vegetation in open and disturbed habitats. The treatment of F. alnus is currently restricted to the roadside, suggesting any individual F. alnus residing within the forest would be left unmanaged and would continue to spread in the area. This research was conducted to determine the spatial patterns and relationship of F. alnus with forest roads. The presence and density of F. alnus at 1412 sample points were recorded on four sites in the Allegheny National Forest, Pennsylvania, USA. Buffer analyses were conducted along roads to determine the relationship between F. alnus density and proximity to forest roads. Geostatistics and spatial analysis by distance indices (SADIE) were used to characterize the spatial pattern of F. alnus. Results of this study showed that F. alnus was spatially aggregated and resided beyond forest roads. Both the density and presence of F. alnus decreased as the distance from the forest road increased. These results imply the potential for precision management of F. alnus by locating and managing only where F. alnus presents.

Comparison of new computational methods for spatial modelling of malaria.

BACKGROUND: Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases. METHODS: This work presents an applied comparison of four proposed 'fast' computational methods for spatial modelling and the software provided to implement them-Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales-country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation. RESULTS: Two of these methods-SpRF and GPBoost-do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods-INLA and FRK-do scale well computationally, however the resulting model fits are very sensitive to the user's modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution. CONCLUSIONS: INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters.

Harvest load transfer sites influence sugarcane billbug (Coleoptera: Curculionidae) spatiotemporal injury in sugarcane.

BACKGROUND: The sugarcane billbug, Sphenophorus levis Vaurie 1978, is a key soil-dwelling insect pest of sugarcane in Brazil and greatly affects plant development and yield. This insect presents an aggregated distribution pattern in production fields. The reasons for such behavior include intraspecific communication and attractivity due to the fermentation of sugar in stalk residues. During mechanized harvesting, part of the harvested material usually falls in the load transfer sites, becoming a potential source for increasing the infestation. We therefore evaluated whether producing areas near the harvest load transfer sites are more prone to S. levis injury. RESULTS: There are greater chances of finding billbug injury within a radius of 740 m from the harvest load transfer site. Additionally, injured areas are estimated to expand 11.96% each growing season. Our spatiotemporal models support higher injured areas surrounding the harvest load transfer site and show clear and significant signs of increased injury levels compared to the initial growing season surveyed. CONCLUSION: Our results reinforce the importance of harvest transfer sites in the dispersion and propagation of the sugarcane billbug. Based on this knowledge, sugarcane millers and growers can adopt preventive and remedial practices within the loading sites that can potentially contribute to the successful management of this insect pest. © 2023 Society of Chemical Industry.

Model-based geostatistical design and analysis of prevalence for soil-transmitted helminths in Kenya: Results from ten-years of the Kenya national school-based deworming programme.

Background: Kenya is endemic for soil-transmitted helminths (STH) with over 6 million children in 27 counties currently at-risk. A national school-based deworming programme (NSBDP) was launched in 2012 with a goal to eliminate parasitic worms as a public health problem. This study used model-based geostatistical (MBG) approach to design and analyse the impact of the NSBDP and inform treatment strategy changes. Methods: A cross-sectional study was used to survey 200 schools across 27 counties in Kenya. The study design, school selection and analysis followed the MBG approach which incorporated historical data on treatment, morbidity and environmental covariates to efficiently predict the helminths prevalence in Kenya. Results: Overall, the NSBDP geographic area prevalence for any STH was estimated to sit between 2 % and <10 % with a high predictive probability of >0.999. Species-specific thresholds were between 2 % and <10 % for Ascaris lumbricoides, 0 % to <2 % for hookworm, and 0 % to <2 % for Trichuris trichiura, all with high predictive probability of >0.999. Conclusions: Based on the World Health Organization guidelines, STH treatment requirements can now be confidently refined. Ten counties may consider suspending treatment and implement appropriate surveillance system, while another 10 will require treatment once every two years, and the remaining seven will require treatment once every year.

Heavy grazing causes plant cluster fragmentation of sparse grasses.

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.

Spatial variability of chemical indicators of Amazon agricultural soils through geomultivariate statistics, Brazil.

This work focuses on evaluating the spatial variability of chemical attributes of soils under different agricultural use and native forest, indicating which are the possible indicator attributes of changes in environmental, through the use and management of the soil. The study was carried out in the southern region of the Amazonas state, in an Argissolo Vermelho-Amarelo (Ultisol). Sampling grids were established measuring: 90 m × 70 m with regular soil collection spacing of 10 m for the guarana and forest areas; 90 m × 56 m spaced at 10 m × 8 m for annatto area; and 54 m × 42 m with spacing between points of 6 m for the cupuaçu area, totaling 80 sampling points in each area, with soil samples collected at depths of 0.0-0.05; 0.05-0.10 m and 0.10-0.20 m. The following attributes were determined: pH, Al3+, K+, Ca2+, Mg2+, P, H + Al, CEC, V% and m%. Descriptive, geostatistical and multivariate statistical analyzes were performed. The results show that it is possible to state that the descriptive, geostatistical and multivariate statistical techniques were able to identify the difference between the spatial variability of the attributes according to each specific use of individual soils. The multivariate analysis made it possible to select the attributes that most contribute to the variability of these soils, and with that, it was found that the forest showed less spatial variability in the surface layer, with higher reach values by scaled semivariograms.

[Source Analysis of Heavy Metals in Typical Farmland Soils Based on PCA-APCS-MLR and Geostatistics].

Human activities often increase the content of heavy metals in surface soils, thus affecting the precise quantification and evaluation of heavy metals in regional soils. In order to systematically study the spatial distribution characteristics and contribution rate of heavy metal pollution sources in typical farmland soil around stone coal mines in western Zhejiang Province, heavy metals such as Cd, Hg, As, Cu, Zn, and Ni in topsoil samples of arable land and agricultural products were collected and analyzed, with an emphasis on the geochemical characteristics of each element and ecological risk assessment of agricultural products. Correlation analysis, principal component analysis (PCA), and the absolute principal component score-multiple linear regression receptor model (APCS-MLR) were used to discuss the source and source contribution rate of soil heavy metal pollution in this area. Meanwhile, the spatial distribution characteristics of the contribution rate of Cd and As pollution sources of the soil in the study area were also expounded in detail by the geostatistical analysis method. The results showed that the contents of six heavy metal elements including Cd, Hg, As, Cu, Zn, and Ni in the study area all exceeded the risk screening value. Among them, two elements exceeded the risk control value, Cd and As, and the point-exceeding rates were 36.11% and 0.69%, respectively. The Cd in agricultural products was also seriously exceeded. According to the analysis, there were two main sources of heavy metal pollution in the soil in the study area. Source one (Cd, Cu, Zn, and Ni) was coming from mining activities and natural sources, and the contribution rates to Cd, Cu, Zn, and Ni were 78.53%, 84.41%, 87%, and 89.13%. Source two (Hg and As) was mainly an industrial source, and the contribution rates to As and Hg were 82.41% and 83.22%, respectively. Cd was the heavy metal with the greatest pollution risk in the study area, and measures should be taken to reduce the pollution risk. There was an abandoned stone coal mine rich in elements such as Cd, Cu, Zn, and Ni. Located in the northeastern part of the study area under the action of atmospheric deposition, the confluence of mine wastewater into irrigation water and farmland sediment was one of the important factors for forming the source of farmland pollution. The settled fly ash was the main pollution source of As and Hg, which was also closely related to agricultural production. The above research can provide technical support for the precise implementation of ecological and environmental management policies.

Integrated processing methodology to identify the spatial variability of accumulated infiltration for agricultural soil

The infiltration of water in the soil, and its variation in space, is essential to establish the irrigation schedule for crops and to evaluate the possible degrading effects on the soil. The objective was to develop an integrated processing methodology in Rstudio to identify the spatial variability of the accumulated infiltration, in two phases related to pea crops. Field sampling was carried out on a rectangular mesh with 48 points per moment, using double infiltrometer rings. The data were evaluated by means of geostatistical tools adjusted with programming code in Rstudio, defining the relationships between the magnitudes of the accumulated infiltration, for different test instants, without the need to make statistical adjustments to the normality of variables, discriminated over a period between 1 and 80 minutes. The results suggest the existence of spatial variability of the accumulated infiltration in the two evaluated phases, considering that most of the analyzed data were adjusted to multiple variance models, maintaining a degree of spatial dependence, and validating the effectiveness of the adjusted methodology developed and implemented. The spatial relationships were corroborated by means of contour maps, where the spatial variation of the accumulated infiltration between the two identified cultivation moments was observed. The reliability of the interpolation by the Ordinary Kriging method was verified by generating variance maps, establishing the degree of homogeneity of the interpolation. The variability of infiltration confirms the validity of the adjusted methodology implemented.

La infiltración del agua en el suelo y su variación espacial es fundamental para establecer la programación de riego en los cultivos y evaluar los posibles efectos degradativos en el suelo. El objetivo fue desarrollar una metodología de procesamiento integrado en Rstudio, para identificar la variabilidad espacial de la infiltración acumulada, en dos fases para un cultivo de arveja. El muestreo de campo se adelantó sobre una malla rectangular georreferenciada con 48 puntos, por cada momento, utilizando anillos infiltrómetros dobles. Los datos fueron evaluados por medio de herramientas geoestadísticas, ajustadas con código de programación en Rstudio, definiendo las relaciones entre las magnitudes de la infiltración acumulada, para diferentes instantes de prueba, sin la necesidad de realizar ajustes estadísticos de normalidad de variables, discriminados en un periodo entre 1 y 80 minutos. Los resultados sugieren la existencia de variabilidad espacial de la infiltración acumulada en las dos fases evaluadas, considerando que, la mayoría de los datos analizados, se ajustaron a múltiples modelos de semivarianza, manteniendo grados de dependencia espacial, particularmente, respecto al máximo valor acumulado de infiltración, validando la eficacia de la metodología ajustada. Las relaciones espaciales fueron corroboradas con mapas de contorno, en donde se observó la variación espacial de la infiltración acumulada entre los momentos de cultivo identificados. La confiabilidad de la interpolación por el método Kriging ordinario, se verificó mediante la generación mapas de varianza, estableciendo el grado de homogeneidad de la interpolación. La variabilidad de la infiltración confirma la validez de la metodología ajustada implementada.

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.

[Spatial Distribution Characteristics and Source Analysis of Heavy Metals in Soil of a Lead Plant in Sanmenxia].

At present, a large-scale relocation of industrial enterprises is taking place in major cities in China, and a large number of contaminated relocation sites are being generated, among which the heavy metal pollution is particularly serious. In order to analyze the pollution status, spatial distribution, and sources of heavy metals in the soil of a lead factory in Sanmenxia, the spatial variation and distribution characteristics of heavy metals in the soil were analyzed using geostatistics, and the main sources of heavy metals in the soil were analyzed using a PMF model. The results showed that the average values of As, Cd, Cu, Pb, Hg, and Ni in the soil far exceeded the background values of the soil environment in Henan province; the contents of As, Cd, Pb, and Hg exceeded the screening values of soil pollution risk; and the contents of As, Pb, and Hg exceeded the control values of soil pollution risk. The high-value area was located on the northern part of the slag yard; the Cr, Ni, and Cd high-value area was located in the north and south of the slag yard; the high-value As area was located in the slag yard between the southern area and the living quarters; the Cu and Pb high-value area was relatively scattered, mainly concentrated in the central part of the raw material storage area and furnace area; and Ni and Cd and Cu and Pb had the same spatial distribution characteristics. Based on the PMF model, it can be seen that there were three main sources of the seven heavy metals, and Cd was mainly from waste residue accumulation, with a contributing rate of 87.60%. Cu, Pb, and Hg were mainly soil parent material, with contribution rates of 92.50%, 75.20%, and 95.40%, respectively. Cr, Ni, and As were mainly raw material dust exhaust gas sources, with contribution rates of 80.80%, 83.30%, and 62.00%, respectively.

Modeling stochastic saline groundwater occurrence in coastal aquifers.

The issue of freshwater salinization in coastal areas has grown in importance with the increase of the demand of groundwater supply and the more frequent droughts. However, the spatial patterns of salinity contamination are not easy to be understood, as well as their numerical modeling is subject to various kinds of uncertainty. This paper offers a robust, flexible, and reliable geostatistical methodology to provide a stochastic assessment of salinity distribution in alluvial coastal areas. The methodology is applied to a coastal aquifer in Campania (Italy), where 83 monitoring wells provided depth-averaged salinity data. A Monte Carlo (MC) framework was implemented to simulate depth-averaged groundwater salinity fields. Both MC stochastic fields and the mean across MC simulations enabled to the delineation of which areas are subject to high salinity. Then, a probabilistic approach was developed setting up salinity thresholds for agricultural use to delineate the areas with unsuitable groundwater for irrigation purposes. Furthermore, steady spatial patterns of saline wedge lengths were unveiled through uncertainty estimates of seawater ingression at the Volturno River mouth. The results were compared versus a calibrated numerical model with remarkable model fit (R2=0.96) and versus an analytical solution, obtaining similar wedge lengths. The results pointed out that the high groundwater salinities found inland (more than 2 km from the coastline) could be ascribed to trapped paleo-seawater rather than to actual seawater intrusion. In fact, the inland high salinities were in correspondence of thick peaty layers, which can store trapped saline waters because of their high porosity and low permeability. Furthermore, these results are consistent with the recognition of depositional environments and the position of ancient lagoon alluvial sediments, located in the same areas where are the highest (simulated) salinity fields. This robust probabilistic approach could be applied to similar alluvial coastal areas to understand spatial patterns of present salinization, to disentangle actual from paleo-seawater intrusion, and more in general to delineate zones with unsuitable salinity for irrigation purposes.