Well site conditions associated with nitrate contamination in a multilayer semiconfined aquifer of Buenos Aires, Argentina

Nitrogen Fertilization. A Review of the Risks Associated with the Inefficiency of Its Use and Policy Responses

Nitrogen (N) is a key input to food production. Nearly half of N fertilizer input is not used by crops and is lost into the environment via emission of gases or by polluting water bodies. It is essential to achieve production levels, which enable global food security, without compromising environmental security. The N pollution level expected by 2050 is projected to be 150% higher than in 2010, with the agricultural sector accounting for 60% of this increase. In this paper, we review the status of the pollution from N fertilizers worldwide and make recommendations to address the situation. The analysis reviews the relationship between N fertilizer use, N use efficiency, no-point pollution, the role of farmer management practices, and policy approaches to address diffuse pollution caused by N fertilization. Several studies show a lack of information as one of the main hurdles to achieve changes in habits. The objective of this study is to highlight the gravity of the current global non-point pollution as well as the need for a communication effort to make farmers aware of the relationship between their activity and N pollution and, therefore, the importance of their fertilizer management practices.

Examining influential drivers of private well users' perceptions in Ontario: A cross-sectional population study

Private well users are responsible for managing and maintaining the quality of their drinking water source. Previous studies in Canada have reported low testing rates among well users, a cornerstone of well stewardship behaviours that can prevent the consumption of contaminated groundwater. To improve well stewardship, it is important to understand the interactions between, and the impacts of, various factors that may influence behaviours. Accordingly, the objective of the current study was to investigate the impact of socio-demographics, property characteristics, and experiences with well construction and acute gastrointestinal illness (AGI) (i.e., previous experiences) on levels of awareness, attitudes, risk perceptions, and beliefs (i.e., risk domains) among private well users in Ontario. A link to a province-wide online survey was circulated between May and August 2018 and novel "risk domain" scoring protocols were developed to classify and summarize response data. The survey was undertaken by 1228 respondents, of which 1030 completed the survey in full. Results indicate a low level of waterborne pathogen awareness, with 50.8% of respondents unaware of any groundwater associated pathogens. Respondents' geographic location, gender, and well type were significantly associated with well users' attitudes and perceptions of risk regarding their personal well water supply and the quality and quantity of local groundwater sources. Higher levels of awareness and lower risk perception scores (i.e., lower perceptions of risk) were associated with residential presence during well construction (p < 0.001 and p = 0.017, respectively). Previous case(s) of AGI within the respondent's household were significantly associated with negative attitudes towards their well water (p < 0.001) and higher risk perception scores (p = 0.025) with respect to the quantity of local groundwater sources. Results may be used to identify critical experiential control points (e.g., during well construction or after a physician confirmed AGI diagnosis) and develop improved risk management and communication strategies aimed at private well users.

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths—an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.

Nitrate pollution of groundwater; all right…, but nothing else?

Contamination from agricultural sources and, in particular, nitrate pollution, is one of the main concerns in groundwater management. However, this type of pollution entails the entrance of other substances into the aquifer, as well as it may promote other processes. In this study, we deal with hydrochemical and isotopic analysis of groundwater samples from four distinct zones in Catalonia (NE Spain), which include 5 different aquifer types, to investigate the influence of fertilization on the overall hydrochemical composition of groundwater. Results indicate that intense fertilizer application, causing high nitrate pollution in aquifers, also homogenize the contents of the major dissolved ions (i.e.; Cl(-), SO4(2-), Ca(2+), Na(+), K(+), and Mg(2+)). Thus, when groundwater in igneous and sedimentary aquifers is compared, significant differences are observed under natural conditions for Cl(-), Na(+) and Ca(2+) (with p-values ranging from <0.001 to 0.038), and when high nitrate concentrations occur, these differences are reduced (most p-values ranged between 0.054 and 0.978). Moreover, positive linear relationships between nitrate and some ions are found indicating the magnitude of the fertilization impact on groundwater hydrochemistry (with R(2) values of 0.490, 0.609 and 0.470, for SO4(2-), Ca(2+) and Cl(-), respectively). Nevertheless, the increasing concentration of specific ions is not only attributed to agricultural pollution, but to their enhancing effect upon the biogeochemical processes that control water-rock interactions. Such results raise awareness that these processes should be evaluated in advance in order to assess an adequate groundwater resources management.

Probability-based nitrate contamination map of groundwater in Kinmen

Groundwater supplies over 50% of drinking water in Kinmen. Approximately 16.8% of groundwater samples in Kinmen exceed the drinking water quality standard (DWQS) of NO3 (-)-N (10 mg/L). The residents drinking high nitrate-polluted groundwater pose a potential risk to health. To formulate effective water quality management plan and assure a safe drinking water in Kinmen, the detailed spatial distribution of nitrate-N in groundwater is a prerequisite. The aim of this study is to develop an efficient scheme for evaluating spatial distribution of nitrate-N in residential well water using logistic regression (LR) model. A probability-based nitrate-N contamination map in Kinmen is constructed. The LR model predicted the binary occurrence probability of groundwater nitrate-N concentrations exceeding DWQS by simple measurement variables as independent variables, including sampling season, soil type, water table depth, pH, EC, DO, and Eh. The analyzed results reveal that three statistically significant explanatory variables, soil type, pH, and EC, are selected for the forward stepwise LR analysis. The total ratio of correct classification reaches 92.7%. The highest probability of nitrate-N contamination map presents in the central zone, indicating that groundwater in the central zone should not be used for drinking purposes. Furthermore, a handy EC-pH-probability curve of nitrate-N exceeding the threshold of DWQS was developed. This curve can be used for preliminary screening of nitrate-N contamination in Kinmen groundwater. This study recommended that the local agency should implement the best management practice strategies to control nonpoint nitrogen sources and carry out a systematic monitoring of groundwater quality in residential wells of the high nitrate-N contamination zones.

Analysis of vulnerability factors that control nitrate occurrence in natural springs (Osona Region, NE Spain)

Nitrate pollution is one of the main concerns of groundwater management in most of the world's agricultural areas. In the Osona region of NE Spain, high concentrations of nitrates have been reported in wells. This study uses the occurrence of this pollutant in natural springs as an indicator of the sub-surface dynamics of the water cycle and shows how groundwater quality is affected by crop fertilization, as an approach to determine the aquifer vulnerability. Nitrate concentration and other hydrochemical parameters based on a biannual database are reported for approximately 80 springs for the period 2004-2009. The background concentration of nitrate is first determined to distinguish polluted areas from natural nitrate occurrence. A statistical treatment using logistic regression and ANOVA is then performed to identify the significance of the effect of vulnerability factors such as the geological setting of the springs, land use in recharge areas, sampling periods, and chemical parameters like pH and EC, on groundwater nitrate pollution. The results of the analysis identify a threshold value of 7-8 mg NO(3)(-)/L for nitrate pollution in this area. Logistic regression and ANOVA results show that an increase in EC or a decrease in pH values is linked to the possibility of higher nitrate concentrations in springs. These analyses also show that nitrate pollution is more dependent on land use than the geological setting of springs or sampling periods. Indeed, the specific geological and soil features of the uppermost layers in their recharge areas do not contribute to the buffering of nitrate impacts on aquifers as measured in natural springs. Land use, and particularly fertilization practices, are major factors in groundwater vulnerability.