Spatial Analysis and Probabilistic Risk Assessment of Exposure to Nitrate in Drinking Water of Abarkouh, Iran

Abstract

Introduction: Several diseases, especially in infants such as some cancer and blue baby are related to the presence of nitrate in drinking water. The Environmental Protection Agency (EPA) specified the maximum contaminant level (MCL) of nitrate as 50 mg L-1 for regulated public water systems. This study aimed to evaluate the concentration of nitrate and to assess its probabilistic risk exposure in drinking water wells of Abarkouh city, Iran.
 Materials and Methods: The average annual nitrate level was studied from 18 wells around Abarkouh in 2017. The Hazard Quotient (HQ) was also investigated as health risk assessment and sensitivity analysis was carried out for effective variables.
 Results: Average concentration of nitrate was 27.57 ± 6.80 mg L-1 and all measured concentrations were below the permitted maximum standard (50 mg L-1) according to the National Standard of Iran. The HQ value for children and adults were more than 1 (1.81) and less than 1, respectively. In calculating HQ for children, the most important variable was the concentration of nitrate in drinking water.
 Conclusion: According to the results, children health is highly at risk in these areas and exposure to nitrate should be reduced for at-risk populations

Probabilistic Risk Assessment of Exposure to Fluoride in Drinking Water in Victoria de Durango, Mexico

“…Sensitivity analysis studies the difference in an output model that may be related to variations in its input elements, indicating the most relevant factors that influence the output model. Figures 5B and 6B show that intake, exposure duration, weight, and concentration are relevant factors that significantly influence the output model, with weight being inversely proportional to risk [28]. Figure 7 shows the risks calculated by the Monte Carlo method associated with the wells distributed to the population.…”

“…The MC method is a quantitative technique that makes use of the probability to imitate, through mathematical models, the random behavior of real phenomena. It examines the role of input parameters as a potential factor for health risk models and determines the output as a probability function of the risk quotient of a particular chemical [28][29][30][31][32]. The findings can be further presented and analyzed by geographic information systems (GIS) according to spatial and temporal variability over a wide study area [31][32][33][34].…”