A novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging.

Soil heavy metal(loid)s contamination caused by rapid urbanization and industrialization seriously affects human health and hinders the global sustainable development goals (SDGs). Currently, there is a lack of comprehensive human health risk assessment (HHRA) studies for multiple land use types at the regional scale. We propose a practical risk assessment framework that integrates empirical Bayesian kriging (EBK), pollution level analyses, and modified HHRA modeling. The concentrations of copper industry-related metals (Cu, Ni, Cd, As, and Hg) in 332 topsoil samples from the south bank of the Yangtze River in Tongling were investigated. Obvious enrichment of Cu, Cd, As, and Hg was detected, and the average concentration of Cu was 5.24 times higher than the background values. The distribution of heavy metal(loid) pollution was typically high in the south and east, and low in the north and west. The mean errors of interpolation for Cu, Ni, and Hg were 0.84, 1.29, and 0, respectively, and the root mean square errors of interpolation for Cd and As were 1.29 and 0.86, respectively. Non-carcinogenic risks of soil heavy metal(loid)s were assessed as acceptable throughout the studied area. The hazard index decreased in the order As (0.448) > Ni (0.0729) > Cd (0.0136) > Hg (9.04 ×10-4) > Cu (6.41 ×10-4). Nevertheless, the carcinogenic risks of Ni, Cd, and As in 70-80% of the administrative units (AUs) were between 10-6 to 10-4, considered an unacceptable level. Exposure through the oral ingestion route accounted for 88.0-99.2% of the total three exposure routes. It is worth noting that four AUs were considered to be the priority control units, and Ni and As were identified as the priority control soil heavy metal(loid)s. This case demonstrates the feasibility and scientific validity of the new EBK-HHRA framework, which confirms that EBK can effectively predict the spatial distribution patterns of soil heavy metal(loid)s and that modified HHRA models are conducive to risk integration at the regional scale. The EBK-HHRA approach is generic and provides substantial support for risk source identification and risk management of soil heavy metal(loid)s contamination at the regional scale.

Accuracy and uncertainty of geostatistical models versus machine learning for digital mapping of soil calcium and potassium.

Accuracy and uncertainty of models used for digital soil mapping are important for assessing confidence of predictions and reliable land use planning and management. In this study, two approaches of geostatistical (spatial) and machine learning (ML) models were evaluated for predictive mapping of soil calcium (Ca) and potassium (K). Two spatial models including empirical Bayesian kriging (EBK) and sequential Gaussian simulation (SGS) were compared with machine learning models: Cubist, random forest (RF) and support vector machine (SVM) in terms of their accuracy and uncertainty for mapping soil Ca and K. The study area is in Nowley, New South Wales, Australia, with an area of 2083 ha and a variety of soil types and farming systems. For the models training process, 240 soil samples data and for validation 102 independent samples data were used. For accuracy assessment R2, root mean square error (RMSE), concordance and bias and for uncertainty assessment confidence limits were investigated. Also, in order to compare the outcomes for the two soil properties with different measurement units, mean absolute percentage error (MAPE) and relative uncertainty (RU) as accuracy and uncertainty measures, respectively, were evaluated. Results showed that for K map SGS had the highest R2 (0.74) and lowest RMSE (1.96), followed by EBK with R2 = 0.72 and RMSE = 2.02. For Ca map, EBK model showed the highest accuracy (R2 = 0.46; RMSE = 3.21), followed by SVM and SGS with comparable accuracies. Comparing the two soil properties, Ca map showed higher MAPE and RU, compared to K map. The lowest MAPE was obtained for EBK model (for K = 39) and SGS model (for K = 44). Also, the lowest RU values were found for EBK and SGS models. Among the ML models, SVM showed lower sensitivity to higher variance in data input. In general, the spatial models outperformed the ML models with regard to both accuracy and uncertainty. An additional conclusion is that considering the data variance in the two soil properties, geostatistical models with lower RU and MAPE were relatively less susceptible to data variance, compared to ML models.

The application of geostatistical analysis and receptor model for the spatial distribution and sources of potentially toxic elements in soils.

This study aimed to identify the spatial patterns of potentially toxic elements (PTEs), including the spatial distribution, spatial autocorrelation, and risk probability, and to quantify the sources of PTEs, to provide guidelines for soil management. Spatial distributions and probabilities of PTEs were determined by empirical Bayesian kriging (EBK), while spatial autocorrelation was estimated by Moran's I. Positive matrix factorization (PMF) was adopted for the quantitative source contributions of PTEs. More than 64.6% of Co, Cr, Mn, and Ni were derived from geogenic sources, with high regions and high-high clusters both correlated to sandstone. Thus, it can be deduced that parent materials dominated the spatial patterns of these PTEs. In addition, some hotspots were situated in urban areas, and the influence of human activities on these four PTEs should be considered. Industry-traffic discharge and parent materials both influenced As, Cu, Pb, and Zn. Nonetheless, the spatial patterns of As, Cu, Pb, and Zn were formed by anthropogenic emissions since hotspots and high-high clusters were contiguously situated in urban areas. 58.5% of Hg originated from atmospheric deposition related to industrial emissions, and 47.2% of Cd was controlled by the application of chemical fertilizers. High levels of Hg and Cd mainly corresponded with industrial sites and cultivated land, suggesting that industrial and geoponic production played major roles in the generation of spatial patterns for Hg and Cd, respectively. Furthermore, the Cd and Hg posed a severe risk to soils, with a high probability to surpass 1.5 times the backgrounds. The EBK, Moran's I, and PMF results showed that all ten PTEs were enriched to some degree due to natural or anthropogenic factors. The results of geostatistical analysis and the receptor model can be mutually verified, indicating the reliability of these methods.

Contamination zoning and health risk assessment of trace elements in groundwater through geostatistical modelling.

Trace elements (TEs) concentration in groundwater is a key factor for health risk assessment (HRA). To achieve high level of accuracy in HRA, the present study performed Monte Carlo simulations, sensitivity analysis and uncertainty analysis to a total of 184 (N = 184) groundwater samples, collected during December 2016 from Birbhum district. TEs in samples were detected by anodic stripping voltammetry (ASV). The mean concentration of TEs were found as Fe (855.88 µg/L)> Zn (204.0 µg/L)> Cu(84.9 µg/L)> Ni(47.31 µg/L)> Pb(14.43 µg/L)> Co(10.58 µg/L)> Cd (7.88 µg/L). It indicated serious contamination by Fe, Cd. Pb and Ni according BIS, 2012. Pollution indicators such as heavy metal pollution index (HPI) revealed that study area is heavily contaminated by these TEs. Incremental lifetime cancer risk (ILCR) value of TEs showed that Cd is the main offender for cancer risk. Average value of total hazard index (THI), was found to be 2.48. THI through ingestion pathways was found to be more risky than dermal contacts accounting for 88% and 12% health hazard respectively. The sensitivity analysis indicated ingestion rate, exposure time, and TEs concentration were the most influential parameters for all groundwater associated health hazards. The TEs affected areas were mapped through Empirical Bayesian Kriging geostatistical model and health risk prone zones were projected. The study demonstrated that Monte Carlo simulation and EBK can provide better accuracy in health risks prediction and spatial distribution analysis of contaminants in any geographical area. The TEs and their hazard zonation mapping with geostatistical modelling will be helpful for the policy makers and researchers to improve groundwater quality management practices.

Trace metals contamination in groundwater and implications on human health: comprehensive assessment using hydrogeochemical and geostatistical methods.

Monitoring the groundwater chemical composition and identifying the presence of pollutants is an integral part of any comprehensive groundwater management strategy. The present study was conducted in a part of West Tripura, northeast India, to investigate the presence and sources of trace metals in groundwater and the risk to human health due to direct ingestion of groundwater. Samples were collected from 68 locations twice a year from 2016 to 2018. Mixed Ca-Mg-HCO3, Ca-Cl and Ca-Mg-Cl were the main groundwater types. Hydrogeochemical methods showed groundwater mineralization due to (1) carbonate dissolution, (2) silicate weathering, (3) cation exchange processes and (4) anthropogenic sources. Occurrence of faecal coliforms increased in groundwater after monsoons. Nitrate and microbial contamination from wastewater infiltration were apparent. Iron, manganese, lead, cadmium and arsenic were above the drinking water limits prescribed by the Bureau of Indian Standards. Water quality index indicated 1.5% had poor, 8.7% had marginal, 16.2% had fair, 66.2% had good and 7.4% had excellent water quality. Correlation and principal component analysis reiterated the sources of major ions and trace metals identified from hydrogeochemical methods. Human exposure assessment suggests health risk due to high iron in groundwater. The presence of unsafe levels of trace metals in groundwater requires proper treatment measures before domestic use.

Optimizing health risk assessment for soil trace metals under low-precision sampling conditions: A case study of agricultural soil.

Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

A comparison of risk factor investigation and experts' opinion elicitation analysis for identifying foot-and-mouth disease (FMD) high-risk areas within the FMD protection zone of South Africa (2007-2016).

Foot-and-mouth disease is a controlled disease in accordance with the South African Animal Diseases Act (Act 35 of 1984). The country was classified by the World Organisation for Animal Health (WOAH) as having a FMD free zone without vaccination in 1996. However, this status was suspended in 2019 due to a FMD outbreak outside the controlled zones. FMD control in South Africa includes animal movement restrictions placed on cloven-hoofed species and products, prophylactic vaccination of cattle, clinical surveillance of susceptible species, and disease control fencing to separate livestock from wildlife reservoirs. The objectives of this study were to evaluate differences in identifying high-risk areas for FMD using risk factor and expert opinion elicitation analysis. Differences in risk between FMD introduction and FMD spread within the FMD protection zone with vaccination (PZV) of South Africa (2007-2016) were also investigated. The study was conducted in the communal farming area of the FMD PZV, which is adjacent to wildlife reserves and characterised by individual faming units. Eleven risk factors that were considered important for FMD occurrence and spread were used to build a weighted linear combination (WLC) score based on risk factor data and expert opinion elicitation. A multivariable conditional logistic regression model was also used to calculate predicted probabilities of a FMD outbreak for all dip-tanks within the study area. Smoothed Bayesian kriged maps were generated for 11 individual risk factors, overall WLC scores for FMD occurrence and spread and for predicted probabilities of a FMD outbreak based on the conditional logistic regression model. Descriptively, vaccine matching was believed to have a great influence on both FMD occurrence and spread. Expert opinion suggested that FMD occurrence was influenced predominantly by proximity to game reserves and cattle density. Cattle populations and vaccination practices were considered most important for FMD spread. Highly effective cattle inspections were observed within areas that previously reported FMD outbreaks, indicating the importance of cattle inspection (surveillance) as a necessary element of FMD outbreak detection. The multivariable conditional logistic regression analysis, which was consistent with expert opinion elicitation; identified three factors including cattle population density (OR 3.87, 95% CI 1.47-10.21) and proximities to game reserve fences (OR 0.82, 95% CI 0.73-0.92) and rivers (OR 1.04, 95% CI 1.01-1.07) as significant factors for reported FMD outbreaks. Regaining and maintaining an FMD-free status without vaccination requires frequent monitoring of high-risk areas and designing targeted surveillance.

Potentially toxic elements in the soils of Campi Flegrei (south Italy) and the immediate surroundings: Spatial distribution, origin and probabilistic human health risk.

Campi Flegrei is an active volcanic field in south Italy where the potentially toxic elements (PTEs) are of growing concern because the intensive anthropogenic and volcanic activities might pose adverse human health effects. In this article, 394 topsoils (0-15 cm) are collected for instrumental analysis of the <2 mm fraction. The geochemical maps indicate that higher concentrations of Pb, Zn, Cd, Cr, Hg, Ni and Sb are related to the urban area, but greater levels of As, Tl, Co, Cu, Se and V are observed in the other parts. A robust principal component analysis detected: (1) the Pb-Zn-Hg-Cd-Sb-Cr-Ni association that probably highlights anthropogenic activities such as heavy traffic load and fossil fuel combustion in the urbanized area; (2) the Al-Fe-Mn-Ti-Tl-V-Co-As-U-Th association that mostly reveals the contribution of pyroclastic deposits; and (3) the Na-K-B association that feasibly indicates the weathering degree. The probabilistic health risk modeling for the children under 6 years old shows negligible Pb and Zn non-carcinogenic risk and unexpected Pb carcinogenic risk for exposure through soil ingestion. However, for the inhalation pathway, the children aged <1 year old have the highest chance (90%) of acceptable (i.e. between 1E-6 and 1E-4) Pb carcinogenic health risk. This should not be overlooked because Naples is under high environmental pressure and previous studies reported the increased Pb and Zn quantities in soil over a 26-year timespan. Overall, the results of geostatistical interpolation, compositional data analysis and probabilistic health risk modeling potentially uncover the link between soil geochemistry and human health.

Sources of polychlorinated biphenyls to Upper Hudson River fish post-dredging.

Polychlorinated biphenyls (PCBs) are persistent, bioaccumulative, and toxic chemicals that are the dominant contaminant in the Upper Hudson River (UHR) in New York State where two General Electric (GE) plants historically discharged PCBs to the river. Portions of the UHR were dredged from 2009 to 2015 to address PCB contamination. In 2017, the first post-dredging survey of yearling feeder fish and sediment PCB contamination was conducted to establish a baseline for the recovery of the river. Prior analysis of the sediment data from the 2017 survey indicated that ∼2% of the PCBs in the surface sediment were higher in molecular weight than the formulation used by GE and therefore arose from non-GE sources. In this work, the fish PCB data from the 2017 survey were analyzed using Positive Matrix Factorization (PMF). Empirical Bayesian Kriging (EBK) was used to estimate PCB concentrations in the sediment at the locations where fish were collected. The results suggest that PCBs that are the products of microbial dechlorination bioaccumulate in the fish and represent 7% of the PCB mass in the fish data set. Further, the results suggest that about 13% of the PCBs in the fish may have come from non-GE sources. This is higher than the percentage of non-GE PCBs in the sediment, but can be explained by the higher molecular weight of the non-GE mixture which causes it to bioaccumulate more effectively than GE PCBs. Concentrations of the non-GE PCBs averaged about 240 ppb wet weight (whole body) in yearling feeder fish. The remedial goals range from 50 to 400 ppb ww in fillet for fish including piscivorous species that are likely to have higher PCB concentrations than feeder fish.

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China.

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM2.5 concentrations. In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression-empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression-empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 µg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 µg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

Spatial Accessibility Assessment of Prehospital EMS with a Focus on the Elderly Population: A Case Study in Ningbo, China.

The spatial accessibility of prehospital EMS is particularly important for the elderly population's physiological functions. Due to the recent expansion of aging populations all over the globe, elderly people's spatial accessibility to prehospital EMS presents a serious challenge. An efficient strategy to address this issue involves using geographic information systems (GIS)-based tools to evaluate the spatial accessibility in conjunction with the spatial distribution of aging people, available road networks, and prehospital EMS facilities. This study employed gravity model and empirical Bayesian Kriging (EBK) interpolation analysis to evaluate the elderly's spatial access to prehospital EMS in Ningbo, China. In our study, we aimed to solve the following specific research questions: In the study area, "what are the characteristics of the prehospital EMS demand of the elderly?" "Do the elderly have equal and convenient spatial access to prehospital EMS?" and "How can we satisfy the prehospital EMS demand of an aging population, improve their spatial access to prehospital EMS, and then ensure their quality of life?" The results showed that 37.44% of patients admitted to prehospital EMS in 2020 were 65 years and older. The rate of utilization of ambulance services by the elderly was 27.39 per 1000 elderly residents. Ambulance use by the elderly was the highest in the winter months and the lowest in the spring months (25.90% vs. 22.38%). As for the disease spectrum, the main disease was found to be trauma and intoxication (23.70%). The mean accessibility score was only 1.43 and nearly 70% of demand points had scored lower than 1. The elderly's spatial accessibility to prehospital EMS had a central-outward gradient decreasing trend from the central region to the southeast and southwest of the study area. Our proposed methodology and its spatial equilibrium results could be taken as a benchmark of prehospital care capacity and help inform authorities' efforts to develop efficient, aging-focused spatial accessibility plans.

Empirical Bayesian kriging implementation and usage.

We described the key features of the pragmatic geostatistical methodology aiming at resolving the following drawbacks of classical geostatistical models: assuming that the data is the realization of a stationary process; assuming that the data values are distributed according to Gaussian distribution; describing the data with a single generating model; not accounting for the model uncertainty in prediction; and not supporting coincident data and individual measurement errors. Our variant of empirical Bayesian kriging (EBK) is a fast and reliable solution for both automatic and interactive data interpolation. It can be used for interpolation of very large datasets up to billions of points. The following features are discussed: the informative prior distribution construction and usage; automatic data transformation of the dependent variable into a Gaussian distribution; data subsetting and merging the estimated models; and interpolation over large areas on the earth's surface. We conducted one simulation experiment and two case studies using highly variable data to investigate the EBK predicting quality.

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia.

OBJECTIVES: The aim of this study was to determine the spatial modeling, seasonal variation of malaria and making prediction map of malaria in northwest Ethiopia. RESULTS: The overall average cumulative annual malaria incidence during the study period was 30 per 100 populations at risk. The highest proportion (29.2%) was observed from June 2015 to October 2016. In temporal analysis of clusters, the epidemic was observed from 2015/7/1 to 2016/12/31 throughout the study period in all districts. Hotspot areas with high clusters (p < 0.001) were observed in Metema district it accounts 18.6% of the total malaria cases. An area of high median predicted incidence proportion (> 50%) was seen in the southwest part of the region. Most of the northern part of the study area was predicted to have a low median incidence proportion (< 10%).

Robust spatial analysis of sequestered metals in a Southern California Bioswale.

Bioswales are a type of permeable green infrastructure designed to slow stormwater and clean runoff by sequestering pollutants such as heavy metals. Measurements of dissolved pollutants before and after the bioswale often justify their ability to clean this runoff, but research addressing the physical and chemical sequestration of these pollutants is scarce. Soil samples were taken from an arid bioswale and analyzed for concentrations of aluminum, cobalt, chromium, copper, iron, magnesium, manganese, nickel, lead, vanadium and zinc. Heat maps of the concentration of these metals in soil were generated via Empirical Bayesian Kriging (EBK) and demonstrate that location-specific sequestration differs between metals within the same swale. Sequential extraction with a modified Tessier et al. (1979) protocol coupled with profiles of metal concentration versus distance along the main flow axis in the bioswale illustrate that the carbonate soil fraction contains elevated concentrations of zinc, lead, cobalt, and manganese, metals sequestered by the bioswale with statistical significance.

Trace elements concentrations in soil, desert-adapted and non-desert plants in central Iran: Spatial patterns and uncertainty analysis.

The concentrations of Cd, Cr and Pb in soil samples and As, Cd, Cr and Pb in plant specimens were analyzed in an arid area in central Iran. Plants were categorized into desert-adapted (Haloxylon ammodendron, Atraphaxis spinosa and Artemisia persica) and non-desert species. It was found that the trace element (TE) accumulating potential of the desert species (Haloxylon ammodendron and Artemisia persica) with a mean value of 0.1 mg kg-1 for Cd was significantly higher than that of the majority of the non-desert species with an average of 0.05 mg kg-1. Artemisia also had a high As accumulating capability with a mean level of 0.8 mg kg-1 in comparison with an average of 0.2 mg kg-1 for most of the other plant species. The mean values of Cr and Pb in Haloxylon ammodendron and Artemisia persica were 5 and 3 mg kg-1, respectively. Among the desert-adapted plants, Atraphaxis proved to be a species with high Cr and Pb accumulating potential, as well. The geoaccumulation index and the overall pollution scores indicated that the highest environmental risk was related to Cd. Different statistical analyses were used to study the spatial patterns of soil Cd and their connections with pollution sources. The variogram was estimated using a classical approach (weighted least squares) and was compared with that of the posterior summaries that resulted from the Bayesian technique, which lay within the 95% Bayesian credible quantile intervals (BIC) of posterior parameter distributions. The prediction of cadmium values at un-sampled locations was implemented by multi-Gaussian kriging and sequential Gaussian simulation methods. The prediction maps showed that the region most contaminated by Cd was the north-eastern part of the study area, which was linked to mining activities, while agricultural influence contributed less in this respect.

Space-time monitoring of coastal pollution in the Gulf of Gaeta, Italy, using δ15N values of Ulva lactuca, landscape hydromorphology, and Bayesian Kriging modelling.

We investigated the space-time dynamics of N pollution in a Mediterranean gulf (Gulf of Gaeta) by means of δ15N variation in seaweed fronds (Ulva lactuca) previously collected from an unpolluted habitat. We used a comprehensive deployment grid that enabled the generation of isotopic seascapes (isoseascapes) describing the topography of N pollution in coastal waters and identifying N input hotspots and their pathways of dispersion at sea. The δ15N values of U. lactuca increased during 48h of exposure to the gulf waters, indicating anthropogenic N inputs from wastewater-derived sources. Comparison of the isoseascapes between two years differing in terms of rainfall identified coastal and offshore areas that were vulnerable to freshwater-transported nutrients, consistent with terrestrial hydromorphology and sea surface-water circulation. Isoseacapes were robust enough to reduce deployment effort, representing a powerful tool for monitoring and management strategies and useful for Environmental Protection Agencies, the main target audience of applied ecological research.

The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014.

BACKGROUND: In Uganda, malaria vector control interventions and case management with Artemisinin Combination Therapies (ACTs) have been scaled up over the last few years as a result of increased funding. Data on parasitaemia prevalence among children less than 5 years old and coverage of interventions was collected during the first two Malaria Indicator Surveys (MIS) conducted in 2009 and 2014, respectively. In this study, we quantify the effects of control interventions on parasitaemia risk changes between the two MIS in a spatio-temporal analysis. METHODS: Bayesian geostatistical and temporal models were fitted on the MIS data of 2009 and 2014. The models took into account geographical misalignment in the locations of the two surveys and adjusted for climatic changes and socio-economic differentials. Parasitaemia risk was predicted over a 2 × 2 km2 grid and the number of infected children less than 5 years old was estimated. Geostatistical variable selection was applied to identify the most important ITN coverage indicators. A spatially varying coefficient model was used to estimate intervention effects at sub-national level. RESULTS: The coverage of Insecticide Treated Nets (ITNs) and ACTs more than doubled at country and sub-national levels during the period 2009-2014. The coverage of Indoor Residual Spraying (IRS) remained static at all levels. ITNs, IRS, and ACTs were associated with a reduction in parasitaemia odds of 19% (95% BCI: 18-29%), 78% (95% BCI: 67-84%), and 34% (95% BCI: 28-66%), respectively. Intervention effects varied with region. Higher socio-economic status and living in urban areas were associated with parasitaemia odds reduction of 46% (95% BCI: 0.51-0.57) and 57% (95% BCI: 0.40-0.53), respectively. The probability of parasitaemia risk decline in the country was 85% and varied from 70% in the North-East region to 100% in Kampala region. The estimated number of children infected with malaria declined from 2,480,373 in 2009 to 825,636 in 2014. CONCLUSIONS: Interventions have had a strong effect on the decline of parasitaemia risk in Uganda during 2009-2014, albeit with varying magnitude in the regions. This success should be sustained by optimizing ITN coverage to achieve universal coverage.

Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran.

Selection of appropriate interpolation methods for the conversion of discrete samples into continuous maps is a controversial issue in the environmental researches. The main objective of this study was to analyze the suitability of three interpolation methods for the discrimination of groundwater with respect to the water quality index (WQI). The groundwater quality data consisted of 17 variables associated with 65 wells located in Andimeshk-Shush Plain. Three spatial interpolation methods including ordinary kriging (OK), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW) were utilized for modeling the groundwater contamination. In addition, different cross-validation indicators were applied to assess the performance of different interpolation methods. The results showed that the performance differed slightly among different methods, although the best performed interpolation method in this study was the empirical Bayesian kriging. Among the interpolation methods, IDW with weighting power of 4 estimated the most contaminated area, while OK estimated the lowest contaminated area. The weighting power of IDW had a significant influence on the estimation, meaning that the estimated contaminated area was increased when a greater weighting power was selected. The subtraction results indicated that there are slightly spatial differences among the contamination assessment results. Results of both standard deviation (SD) and coefficient of variation (CV) also showed that uncertainty was highest in the southern part of the study area, where the distribution of wells were more intensive than that of the northern part.

Changing spatial patterns and increasing rurality of HIV prevalence in the Democratic Republic of the Congo between 2007 and 2013.

The Democratic Republic of the Congo (DRC) has one of the lowest HIV prevalence in sub-Saharan Africa, estimated at 1.1% [0.9-1.3] of adults aged 15-49 in 2013 (UNAIDS). Within the 2 million km(2) country, however, there exists spatial variation in HIV prevalence, with the highest HIV prevalence observed in the large cities of Kinshasa and Lubumbashi. Globally, HIV is an increasingly rural disease, diffusing outwards from urban centers of high HIV prevalence to places where HIV was previously absent or present at very low levels. Utilizing data collected during Demographic and Health Surveillance (DHS) in 2007 and 2013 in the DRC, we sought to update the map of HIV prevalence in the DRC as well as to explore whether HIV in the DRC is an increasingly rural disease or remains confined to urban areas. Bayesian kriging and regression indicate that HIV prevalence in rural areas of the DRC is higher in 2013 than in 2007 and that increased distance to an urban area is no longer protective against HIV as it was in 2007. These findings suggest that HIV education, testing and prevention efforts need to diffuse from urban to rural areas just as HIV is doing.

On the use of posterior predictive probabilities and prediction uncertainty to tailor informative sampling for parasitological surveillance in livestock.

Model-based geostatistics and Bayesian approaches are appropriate in the context of Veterinary Epidemiology when point data have been collected by valid study designs. The aim is to predict a continuous infection risk surface. Little work has been done on the use of predictive infection probabilities at farm unit level. In this paper we show how to use predictive infection probability and related uncertainty from a Bayesian kriging model to draw a informative samples from the 8794 geo-referenced sheep farms of the Campania region (southern Italy). Parasitological data come from a first cross-sectional survey carried out to study the spatial distribution of selected helminths in sheep farms. A grid sampling was performed to select the farms for coprological examinations. Faecal samples were collected for 121 sheep farms and the presence of 21 different helminths were investigated using the FLOTAC technique. The 21 responses are very different in terms of geographical distribution and prevalence of infection. The observed prevalence range is from 0.83% to 96.69%. The distributions of the posterior predictive probabilities for all the 21 parasites are very heterogeneous. We show how the results of the Bayesian kriging model can be used to plan a second wave survey. Several alternatives can be chosen depending on the purposes of the second survey: weight by posterior predictive probabilities, their uncertainty or combining both information. The proposed Bayesian kriging model is simple, and the proposed samping strategy represents a useful tool to address targeted infection control treatments and surbveillance campaigns. It is easily extendable to other fields of research.