The aim of the paper is to compare nitrate concentrations in samples of supply water as well as water from deep and dug wells located in the eastern region of Poland. Additionally, samples of bottled water (spring and natural mineral), certifi ed by the Institute of Mother and Child and the Children’s Memorial Health Institute, were subjected to analyses. On the basis of the obtained results, health risks related to the occurrence of methemoglobinemia in neonates and infants were evaluated. The risk analysis was performed according to the procedure recommended by the United States Environmental Protection Agency. Nitrate concentrations in the examined samples ranged from: 0.153–161.1 mg/l. The lowest concentration of nitrates was determined in the samples of bottled water, the highest being detected in the water from dug wells. It was found that nitrate concentration in samples of bottled and supply water did not pose any risk to the health of neonates and infants. The highest health risk related to methemoglobinemia occurs for neonates consuming water originating from dug wells. The risk decreases along with the age of an infant.
Nutrient pollution such as nitrate (NO3−) can cause water quality degradation in rivers used as a source of drinking water. This situation raises the question of how the nutrients have moved depending on many factors such as land use and anthropogenic sources. Researchers developed several nutrient export coefficient models depending on the aforementioned factors. To this purpose, statistical data including a number of factors such as historical water quality and land use data for the Melen Watershed were used. Nitrate export coefficients are estimates of the total load or mass of nitrate (NO3−) exported from a watershed standardized to unit area and unit time (e.g. kg/km2/day). In this study, nitrate export coefficients for the Melen Watershed were determined using the model that covers the Frequentist and Bayesian approaches. River retention coefficient was determined and introduced into the model as an important variable.