A Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design.

Globally, there are over 1 billion people infected with soil-transmitted helminths (STHs), mostly living in marginalized settings with inadequate sanitation in sub-Saharan Africa and Southeast Asia. The World Health Organization recommends an integrated approach to STH morbidity control through improved access to sanitation and hygiene education and the delivery of preventive chemotherapy (PC) to school-age children delivered through schools. Progress of STH control programs is currently estimated using a baseline (pre-PC) school-based prevalence survey and then monitored using periodical school-based prevalence surveys, known as Impact Assessment Surveys (IAS). We investigated whether integrating geostatistical methods with a Markov model or a mechanistic transmission model for projecting prevalence forward in time from baseline can improve IAS design strategies. To do this, we applied these 2 methods to prevalence data collected in Kenya, before evaluating and comparing their performance in accurately informing optimal survey design for a range of IAS sampling designs. We found that, although both approaches performed well, the mechanistic method more accurately projected prevalence over time and provided more accurate information for guiding survey design. Both methods performed less well in areas with persistent STH hotspots where prevalence did not decrease despite multiple rounds of PC. Our findings show that these methods can be useful tools for more efficient and accurate targeting of PC. The general framework built in this paper can also be used for projecting prevalence and informing survey design for other neglected tropical diseases.

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.

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.

Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.

The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neural networks (NN) are emerging as a new candidate for performing these tasks as an alternative to conventional geostatistical tools such as Co-Kriging. This study demonstrates the implementation of a NN for estimating radiological values such as ambient dose equivalent (H*(10)), surface activity and activity concentrations of natural radionuclides present in a waste dump of a Cu mine with a high level of natural radionuclides. The results obtained using a NN were compared with those estimated by Co-Kriging. Both models reproduced field measurements equivalently as a function of spatial coordinates. Similarly, the deviations from the reference concentration values obtained in the output layer of the NN were smaller than the deviations obtained from the multiple regression analysis (MRA), as indicated by the results of the root mean square error. Finally, the method validation showed that the estimation of radiological parameters based on their spatial coordinates faithfully reproduced the affected area. The estimation of the activity concentrations was less accurate for both the NN and MRA; however, both methods gave statistically comparable results for activity concentrations obtained by gamma spectrometry (Student's t-test and Fisher's F-test).

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.

Mapping of on-field soil nutrient variabilities as a guiding force for smart farming: a case study from FarmerZone sentinel-1 from three potato agroecological zones of India.

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming.

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.

A Geostatistical Framework Predicting Zooplankton Abundance in a Large River: Management Implications towards Potamoplankton Sustainability.

The zooplankton community is a widely used bioindicator for the biological assessment of riverine aquatic ecosystems. Phyto-zooplankton interaction and spatially varying river environment parameters perceivably govern their spatial distribution in a large river. This invites the challenge of predicting zooplankton abundance along the river channel. The present article has proposed a geostatistical framework to predict zooplankton abundance along the river course while decoupling phyto-zooplankton relationship from spatial dependency. The strength of secondary data on the river Narmada-a large tropical river in India-has been utilised to accomplish the goal. The nonlinear logistic regression kriging has been found to be the most effective framework. The phyto-zooplankton relationship captured 66% of zooplankton variability, having moderate (37%) residual spatial dependence. The results have shown longitudinally fluctuating spatial variability, which supports the river serial discontinuity concept. The proposed framework has generated smooth zooplankton abundance and sustainability predictive maps that have allowed detection of the change point locations of zooplankton abundance. The map has precisely identified the most productive zone of zooplankton sustainability. The study also has appraised obtaining approximate data in the areas where sampling is infeasible, which will be helpful for location-specific management strategies on a lower spatial scale.

Ecotoxicology and geostatistical techniques employed in subtropical reservoirs sediments after decades of copper sulfate application.

Spatial distribution linked to geostatistical techniques contributes to sum up information into an easier-to-comprehend knowledge. This study compares copper spatial distribution in surface sediments and subsequent categorization according to its toxicological potential in two reservoirs, Rio Grande (RG) and Itupararanga (ITU) (São Paulo-Brazil), where copper sulfate is applied and not applied, respectively. Sediments from 47 sites in RG and 52 sites in ITU were collected, and then, copper concentrations were interpolated using geostatistical techniques (kriging). The resulting sediment distributions were classified in categories based on sediment quality guides: threshold effect level and probable effect level; regional reference values (RRVs) and enrichment factor (EF). Copper presented a heterogenic distribution and higher concentrations in RG (2283.00 ± 1308.75 mg/kg) especially on the upstream downstream, associated with algicide application as well as the sediment grain size, contrary to ITU (21.81 ± 8.28 mg/kg) where a no-clear pattern of distribution was observed. Sediments in RG are predominantly categorized as "Very Bad", whereas sediments in ITU are mainly categorized as "Good", showing values higher than RRV. The classification is supported by the EF categorization, which in RG is primarily categorized as "Very High" contrasting to ITU classified as "Absent/Very Low". Copper total stock in superficial sediment estimated for RG is 4515.35 Ton of Cu and for ITU is 27.45 Ton of Cu.

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.

Heavy grazing reduced the spatial heterogeneity of Artemisia frigida in desert steppe.

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.

Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000-2018.

BACKGROUND: Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sex-specific 5-year age groups for adults ages 15-59 years across SSA. METHODS: We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units. RESULTS: We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group. CONCLUSIONS: As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA.

A simulation study of disaggregation regression for spatial disease mapping.

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While these simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

Ozone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health.

Tropospheric ozone (O3) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O3 concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of O3over large areas is currently an open challenge. Thus, one of the final objectives of O3 modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O3 modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingO3concentrationsat the local scale, where enough O3concentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred.

Spatial variation and inequities in antenatal care coverage in Kenya, Uganda and mainland Tanzania using model-based geostatistics: a socioeconomic and geographical accessibility lens.

BACKGROUND: Pregnant women in sub-Saharan Africa (SSA) experience the highest levels of maternal mortality and stillbirths due to predominantly avoidable causes. Antenatal care (ANC) can prevent, detect, alleviate, or manage these causes. While eight ANC contacts are now recommended, coverage of the previous minimum of four visits (ANC4+) remains low and inequitable in SSA. METHODS: We modelled ANC4+ coverage and likelihood of attaining district-level target coverage of 70% across three equity stratifiers (household wealth, maternal education, and travel time to the nearest health facility) based on data from malaria indicator surveys in Kenya (2020), Uganda (2018/19) and Tanzania (2017). Geostatistical models were fitted to predict ANC4+ coverage and compute exceedance probability for target coverage. The number of pregnant women without ANC4+ were computed. Prediction was at 3 km spatial resolution and aggregated at national and district -level for sub-national planning. RESULTS: About six in ten women reported ANC4+ visits, meaning that approximately 3 million women in the three countries had 20,000 women having

Hydro-geochemical analysis based on entropy and geostatistics model for delineation of anthropogenic ground water pollution for health risks assessment of Dhenkanal district, India.

Consumption of poor quality water causes serious human health hazards. Therefore, it is very crucial to investigate factors influencing the quality of groundwater and its suitability for drinking purpose. In the present study, groundwater quality of the Dhenkanal district of Odisha, India was characterized and the spatial distribution of different water quality parameters were analyzed using the multivariate statistics, entropy theory, and geostatistics techniques. In the present study 112 number of groundwater tube well samples were collected from the study area. The entropy theory revealed that SO42-, Mg+2 and Cl- were the most influencing parameters. A similar observation was also observed based on the correlation coefficient analysis. Groundwater quality index (GWQI) and entropy-weighted water quality index (EWQI) classifications indicated that 78.57 and 43.75% of the collected groundwater samples were categorized under excellent water quality, whereas, the rest of the samples were varying from good to medium drinking water quality. In addition, the result of EWQI classification offers more realistic assessment than that of GWQIs owing to its high precision, simplicity and without application of artificial weight. The correlation coefficient between Ca+2 and HCO3-, Mg+2 and PO4- were significantly high which might be due the presence of CaHCO3 and MgPO4 in the groundwater samples. The GWQI revealed a weak spatial dependence of groundwater quality.

Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context.

The Internet of Things consists of "things" made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things. In addition, these applications are concerned with the quality of service offered by the sensor network or the correct analysis method that can lead to inaccurate or irrelevant conclusions, which can cause significant harm for decision makers. In this context, we propose two systematic methods to analyze spatially distributed data Internet of Things. We show with the results that geostatistics and spatial statistics are more appropriate than classical statistics to do this analysis.

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

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

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

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

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

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