Spatial investigation of the links between aflatoxins legislation, climate, and liver cancer at the global scale.

Aflatoxins are carcinogenic toxins produced by fungi, and many countries legislate limits in food. Previous research suggests elevated liver cancer (LC) mortality in some areas may be due to aflatoxin exposure, but this has not been investigated spatially. We investigate links between aflatoxin legislation, climate, and LC mortality and other covariates globally. Comparison tests of LC mortality showed expected patterns with legislation and climate. They also showed associations between high LC mortality and high Hepatitis, low alcohol consumption, low health expenditure and high family agriculture rates. Spatial analysis showed latitudinal trend with significant clusters of low LC mortality in Europe and high rates in West Africa, Central America, East and South-East Asia. Only health expenditure and Hepatitis were significant in spatial regression, but climate and family agriculture were also significant in multiple linear regression (MLR). Results suggest that aflatoxin education and legislation should be expanded, particularly in hot/wet climates.

Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations.

Aflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall < 50 mm, the 30-year normals for the region. Future climate data were estimated for each year (2000-2100) and county in southern GA using the RCP 4.5 and RCP 8.5 emissions scenarios. The number of counties with June maximum temperatures > 33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010-2040 compared to 2000-2030 suggesting that adaptation strategies should be employed as soon as possible.

Investigation of the Environmental and Socio-Economic Characteristics of Counties with a High Asthma Burden to Focus Asthma Action in Utah.

Rising adult asthma prevalence (AAP) rates and asthma emergency room (AER) visits constitute a large burden on public health in Utah (UT), a high-altitude state in the Great Basin Desert, USA. This warrants an investigation of the characteristics of the counties with the highest asthma burden within UT to improve allocation of health resources and for planning. The relations between several predictor environmental, health behavior and socio-economic variables and two health outcome variables, AAP and AER visits, were investigated for UT's 29 counties. Non-parametric statistical comparison tests, correlation and linear regression analysis were used to determine the factors significantly associated with AER visits and AAP. Regression kriging with Utah small area data (USAD) as well as socio-economic and pollution data enabled local Moran's I cluster analysis and the investigation of moving correlations between health outcomes and risk factors. Results showed the importance of desert/mining dust and socio-economic status as AAP and AER visits were greatest in the south of the state, highlighting a marked north-south divide in terms of these factors within the state. USAD investigations also showed marked differences in pollution and socio-economic status associated with AAP within the most populous northern counties. Policies and interventions need to address socio-economic inequalities within counties and between the north and south of the state. Fine (PM2.5) and coarse (PM10) particulate matter monitors should be installed in towns in central and southern UT to monitor air quality as these are sparse, but in the summer, air quality can be worse here. Further research into spatiotemporal variation in air quality within UT is needed to inform public health interventions such as expanding clean fuel programs and targeted land-use policies. Efforts are also needed to examine barriers to routine asthma care.

The relevant range of scales for multi-scale contextual spatial modelling.

Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling.

Spatial analysis of drug poisoning deaths in the American west: A comparison study using profile regression to adjust for collinearity and spatial correlation.

BACKGROUND: The USA has seen dramatic increases in drug poisoning deaths (DPD) recently. State-level rates have responded to federal and state initiatives, yet the counties with the highest rates are stable. Spatial analysis enables investigators to identify the highest risk counties and most important risk factors, although results are often confounded by spatial autocorrelation and multicollinearity. METHODS: Profile regression (PR) is an integrated method for cluster and regression analysis, which adjusts for spatial-autocorrelation and multi-collinearity. RESULTS: With PR, three clusters were identified in the Western USA with most of NM, NV and UT and several counties in AZ, CO, ID and WY being high-risk. Cluster analysis in a previous study only identified high-risk counties in northern CA, NM and NV. Elevation, suicide and LDS population were positively, and population density was negatively linked with DPD for PR and standard regression (SR) showing differences between the mountain west and coastal areas. Complex relationships between DPD and several variables were identified by PR which was not possible with SR. CONCLUSIONS: Statistically principled methods like PR are needed for appropriate identification of the highest risk counties and important risk factors given the complex relationships with DPD. Funding for prevention, education and medical services should be targeted at rural, mountain communities in the west which have high %LDS and suicide rates. Counties with high %poverty and %Hispanic were also at high-risk. Individual-level studies are needed to confirm important risk factors in high-risk counties.

Climate-induced changes in continental-scale soil macroporosity may intensify water cycle.

Soil macroporosity affects field-scale water-cycle processes, such as infiltration, nutrient transport and runoff1,2, that are important for the development of successful global strategies that address challenges of food security, water scarcity, human health and loss of biodiversity3. Macropores-large pores that freely drain water under the influence of gravity-often represent less than 1 per cent of the soil volume, but can contribute more than 70 per cent of the total soil water infiltration4, which greatly magnifies their influence on the regional and global water cycle. Although climate influences the development of macropores through soil-forming processes, the extent and rate of such development and its effect on the water cycle are currently unknown. Here we show that drier climates induce the formation of greater soil macroporosity than do more humid ones, and that such climate-induced changes occur over shorter timescales than have previously been considered-probably years to decades. Furthermore, we find that changes in the effective porosity, a proxy for macroporosity, predicted from mean annual precipitation at the end of the century would result in changes in saturated soil hydraulic conductivity ranging from -55 to 34 per cent for five physiographic regions in the USA. Our results indicate that soil macroporosity may be altered rapidly in response to climate change and that associated continental-scale changes in soil hydraulic properties may set up unexplored feedbacks between climate and the land surface and thus intensify the water cycle.

Investigating the Role of Wind in the Dispersion of Heavy Metals Around Mines in Arid Regions (a Case Study from Kushk Pb-Zn Mine, Bafgh, Iran).

The Kushk Pb-Zn mine is located in Central Iran and it has been in operation for the last 75 years. To investigate the role of wind dispersion of heavy metal pollutants from the mine area, dust samples were collected during 1 year and topsoil samples were collected around the mine. Results showed that the topsoil is polluted with Pb and Zn to about 1500 m away from the mine. It was also found that there was not a significant difference between the metal concentrations in topsoil and dust samples. The Pb and Zn concentrations in the dust samples exceeded 200 mg kg-1 and their lateral dispersion via wind was estimated to be about 4 km away from the mine. It has been shown that a combination of mining activities and mechanical dispersion via water and wind have caused lateral movement of heavy metals in this area.

Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa.

The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission.

Monitoring and assessment of soil erosion at micro-scale and macro-scale in forests affected by fire damage in northern Iran.

Understanding the occurrence of erosion processes at large scales is very difficult without studying them at small scales. In this study, soil erosion parameters were investigated at micro-scale and macro-scale in forests in northern Iran. Surface erosion and some vegetation attributes were measured at the watershed scale in 30 parcels of land which were separated into 15 fire-affected (burned) forests and 15 original (unburned) forests adjacent to the burned sites. The soil erodibility factor and splash erosion were also determined at the micro-plot scale within each burned and unburned site. Furthermore, soil sampling and infiltration studies were carried out at 80 other sites, as well as the 30 burned and unburned sites, (a total of 110 points) to create a map of the soil erodibility factor at the regional scale. Maps of topography, rainfall, and cover-management were also determined for the study area. The maps of erosion risk and erosion risk potential were finally prepared for the study area using the Revised Universal Soil Loss Equation (RUSLE) procedure. Results indicated that destruction of the protective cover of forested areas by fire had significant effects on splash erosion and the soil erodibility factor at the micro-plot scale and also on surface erosion, erosion risk, and erosion risk potential at the watershed scale. Moreover, the results showed that correlation coefficients between different variables at the micro-plot and watershed scales were positive and significant. Finally, assessment and monitoring of the erosion maps at the regional scale showed that the central and western parts of the study area were more susceptible to erosion compared with the western regions due to more intense crop-management, greater soil erodibility, and more rainfall. The relationships between erosion parameters and the most important vegetation attributes were also used to provide models with equations that were specific to the study region. The results of this paper can be useful for better understanding erosion processes at the micro-scale and macro-scale in any region having similar vegetation attributes to the forests of northern Iran.

Spatial analysis of drug poisoning deaths in the American West, particularly Utah.

BACKGROUND: Most states in the Western US have high rates of drug poisoning death (DPD), especially New Mexico, Nevada, Arizona and Utah (UT). This seems paradoxical in UT where illicit drug use, smoking and drinking rates are low. To investigate this, spatial analysis of county level DPD data and other relevant factors in the Western US and UT was undertaken. METHODS: Poisson kriging was used to smooth the DPD data, populate data gaps and improve the reliability of rates recorded in sparsely populated counties. Links between DPD and economic, environmental, health, lifestyle, and demographic factors were investigated at four scales using multiple linear regression. LDS church membership and altitude, factors not previously considered, were included. Spatial change in the strength and sign of relationships was investigated using geographically weighted regression and significant DPD clusters were identified using the Local Moran's I. RESULTS: Economic factors, like the sharp social gradient between rural and urban areas were important to DPD throughout the west. Higher DPD rates were also found in areas of higher elevation and the desert rural areas in the south. The unique characteristics of DPD in UT in terms of health and lifestyle factors, as well as the demographic structure of DPD in the most LDS populous states (UT, Idaho, Wyoming), suggest that high DPD in heavily LDS areas are predominantly prescription opioid related whereas in other Western states a larger proportion of DPD might come from illicit drugs. CONCLUSION: Drug policies need to be adapted to the geographical differences in the dominant type of drug causing death. Educational materials need to be marketed to the demographic groups at greatest risk and take into account differences in population characteristics between and within States. Some suggestions about how such adaptations can be made are given and future research needs outlined.

A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa.

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs.

Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale.

Legacy data in the form of soil maps, which often have typical property measurements associated with each polygon, can be an important source of information for digital soil mapping (DSM). Methods of disaggregating such information and using it for quantitative estimation of soil properties by methods such as regression kriging (RK) are needed. Several disaggregation processes have been investigated; preferred methods include those which include consideration of scorpan factors and those which are mass preserving (pycnophylactic) making transitions between different scales of investigation more theoretically sound. Area to point kriging (AtoP kriging) is pycnophylactic and here we investigate its merits for disaggregating legacy data from soil polygon maps. Area to point regression kriging (AtoP RK) which incorporates ancillary data into the disaggre-gation process was also applied. The AtoP kriging and AtoP RK approaches do not involve collection of new soil measurements and are compared with disaggregation by simple rasterization. Of the disaggregation methods investigated, AtoP RK gave the most accurate predictions of soil organic carbon (SOC) concentrations (smaller mean absolute errors (MAEs) of cross-validation) for disaggregation of soil polygon data across the whole of Northern Ireland. Legacy soil polygon data disaggregated by AtoP kriging and simple rasterization were used in a RK framework for estimating soil organic carbon (SOC) concentrations across the whole of Northern Ireland, using soil sample data from the Tellus survey of Northern Ireland and with other covariates (altitude and airborne radiometric potassium). This allowed direct comparison with previous analysis of the Tellus survey data. Incorporating the legacy data, whether from simple rasterization of the polygons or AtoP kriging, substantially reduced the MAEs of RK compared with previous analyses of the Tellus data. However, using legacy data disaggregated by AtoP kriging in RK resulted in a greater reduction in MAEs. A jack-knife procedure was also performed to determine a suitable number of additional soil samples that would need to be collected for RK of SOC for the whole of Northern Ireland depending on the availability of ancillary data. We recommend i) if only legacy soil polygon map data are available, they should be disaggregated using AtoP kriging, ii) if ancillary data are also available legacy data should be disaggregated using AtoP RK and iii) if new soil measurements are available in addition to ancillary and legacy soil map data, the legacy soil map data should be first disaggregated using AtoP kriging and these data used along with ancillary data as the fixed effects for RK of the new soil measurements.

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States.

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.