Crop production is the single largest cause of human alteration of the global nitrogen cycle. We present a comprehensive assessment of global nitrogen flows in cropland for the year 2000 with a spatial resolution of 5 arc-minutes. We calculated a total nitrogen input (IN) of 136.60 trillion grams (Tg) of N per year, of which almost half is contributed by mineral nitrogen fertilizers, and a total nitrogen output (OUT) of 148.14 Tg of N per year, of which 55% is uptake by harvested crops and crop residues. We present high-resolution maps quantifying the spatial distribution of nitrogen IN and OUT flows, soil nitrogen balance, and surface nitrogen balance. The high-resolution data are aggregated at the national level on a per capita basis to assess nitrogen stress levels. The results show that almost 80% of African countries are confronted with nitrogen scarcity or nitrogen stress problems, which, along with poverty, cause food insecurity and malnutrition. The assessment also shows a global average nitrogen recovery rate of 59%, indicating that nearly two-fifths of nitrogen inputs are lost in ecosystems. More effective management of nitrogen is essential to reduce the deleterious environmental consequences.global nitrogen cycle | malnutrition | nitrogen scarcity | soil fertility

Selenium is a natural trace element that is of fundamental importance to human health. The extreme geographical variation in selenium concentrations in soils and food crops has resulted in significant health problems related to deficient or excess levels of selenium in the environment. To deal with these kinds of problems in the future it is essential to get a better understanding of the processes that control the global distribution of selenium. The recent development of analytical techniques and methods enables accurate selenium measurements of environmental concentrations, which will lead to a better understanding of biogeochemical processes. This improved understanding may enable us to predict the distribution of selenium in areas where this is currently unknown. These predictions are essential to prevent future Se health hazards in a world that is increasingly affected by human activities.

Contamination of groundwaters with geogenic arsenic poses a major health risk to millions of people. Although the main geochemical mechanisms of arsenic mobilization are well understood, the worldwide scale of affected regions is still unknown. In this study we used a large database of measured arsenic concentration in groundwaters (around 20,000 data points) from around the world as well as digital maps of physical characteristics such as soil, geology, climate, and elevation to model probability maps of global arsenic contamination. A novel rule-based statistical procedure was used to combine the physical data and expert knowledge to delineate two process regions for arsenic mobilization: "reducing" and "high-pH/ oxidizing". Arsenic concentrations were modeled in each region using regression analysis and adaptive neuro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty propagation to produce probability maps. The derived global arsenic models could benefit from more accurate geologic information and aquifer chemical/physical information. Using some proxy surface information, however, the models explained 77% of arsenic variation in reducing regions and 68% of arsenic variation in high-pH/oxidizing regions. The probability maps based on the above models correspond well with the known contaminated regions around the world and delineate new untested areas that have a high probability of arsenic contamination. Notable among these regions are South East

Arsenic contamination of shallow groundwater is among the biggest health threats in the developing world. Targeting uncontaminated deep aquifers is a popular mitigation option although its long-term impact remains unknown. Here we present the alarming results of a large-scale groundwater survey covering the entire Red River Delta and a unique probability model based on three-dimensional Quaternary geology. Our unprecedented dataset reveals that ∼7 million delta inhabitants use groundwater contaminated with toxic elements, including manganese, selenium, and barium. Depth-resolved probabilities and arsenic concentrations indicate drawdown of arsenic-enriched waters from Holocene aquifers to naturally uncontaminated Pleistocene aquifers as a result of >100 years of groundwater abstraction. Vertical arsenic migration induced by large-scale pumping from deep aquifers has been discussed to occur elsewhere, but has never been shown to occur at the scale seen here. The present situation in the Red River Delta is a warning for other As-affected regions where groundwater is extensively pumped from uncontaminated aquifers underlying high arsenic aquifers or zones.three-dimensional risk modeling | anthropogenic influence | drinking water resources | geogenic contamination | health threat

The use of groundwater with high fluoride concentrations poses a health threat to millions of people around the world. This study aims at providing a global overview of potentially fluoriderich groundwaters by modeling fluoride concentration. A large database of worldwide fluoride concentrations as well as available information on related environmental factors such as soil properties, geological settings, and climatic and topographical information on a global scale have all been used in the model. The modeling approach combines geochemical knowledge with statistical methods to devise a rule-based statistical procedure, which divides the world into 8 different "process regions". For each region a separate predictive model was constructed. The end result is a global probability map of fluoride concentration in the groundwater. Comparisons of the modeled and measured data indicate that 60-70% of the fluoride variation could be explained by the models in six process regions, while in two process regions only 30% of the variation in the measured data was explained. Furthermore, the global probability map corresponded well with fluorotic areas described in the international literature. Although the probability map should not replace fluoride testing, it can give a first indication of possible contamination and thus may support the planning process of new drinking water projects.

Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Although many arsenic-affected areas have been identified in recent years, a systematic evaluation of vulnerable areas remains to be carried out. Here we present maps pinpointing areas at risk of groundwater arsenic concentrations exceeding 10 μgL-1. These maps were produced by combining geological and surface soil parameters in a logistic regression model, calibrated with 1756 aggregated and geo-referenced groundwater data points from the Bengal, Red River and Mekong deltas. We show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and that the combination of surface parameters is a successful approach to predict groundwater arsenic contamination. Predictions are in good agreement with the known spatial distribution of arsenic contamination and further indicate elevated risks in Sumatra and Myanmar where no groundwater studies exist.

Water resources sustainability has the main contribution to the existence and durability of the farming systems and strongly depends on the cropping pattern practices. A comprehensive cropping pattern planning takes in to account the high level of interrelation of the environmental, economic and social aspects of farming systems. In order to assess the sustainability of water resources and determine an optimal pattern of cropping in a rural farming system, this paper introduces two ratios of "net return/water consumption" and "labor employment/water consumption" and attempts to simultaneously optimize them as the sustainability indicators. To this purpose, a multi-objective fractional goal programming (MOFGP) procedure is considered as the main approach of the study to be accomplished by several other single and multi-objective linear and fractional programming models. The results show that the FP models are more significant to contribute in assessing the sustainability indicators compared to the LP models, and the MOFGP solution is considered better, compared to the single objective FP solutions. The results will be illustrated quantitatively.

Mining-contaminated sites and the affected communities at risk are important issues on the agenda of both researchers and policy makers, particularly in the former communist block countries in Eastern Europe. Integrated analyses and expert based assessments concerning mining affected areas are important in providing solid policy guidelines for environmental and social risk management and mitigation. Based on a survey for 103 households conducted in a former mining site in the Certej Catchment of the Apuseni Mountains, western Romania, this study assesses local communities' perceptions on the quality of water in their living area. Logistic regression was used to examine peoples' perception on the quality of the main river water and of the drinking water based on several predictors relating to social and economic conditions. The results from the perception analysis were then compared with the measurements of heavy metal contamination of the main river and drinking water undertaken in the same study area. The findings indicate that perception and measurement results for the water quality in the Certej Catchment are convergent, suggesting an obvious risk that mining activities pose on the surface water. However, the perception on drinking water quality was little predicted by the regression model and does not seem to be so much related to mining as to other explanatory factors, such as special mineralogy of rock and soils or improper water treatment infrastructure, facts suggested by the measurements of the contaminants. Discussion about the implications of these joint findings for risk mitigation policies completes this article.