## 3 Results and Discussion
### A Probabilistic Approach for Nitrate Risk Assessment  
Monte Carlo simulation (MCS) can evaluate the health risk associated with nitrate on human health by analyzing the variability and uncertainty of many factors. Oracle Crystal Ball (version 11.1.2.4.850) was utilized for the simulation, conducting  
Figure 8: Values of total hazard quotient (HQ) for post-irrigation period for adult males, adult females, and children. 20,000 iterations. The exposure risk and point value were computed by selecting parameter values from their respective distributions using this method. The upper confidence level (p95) of nitrate was ascertained for adult males, adult females, and children using this probabilistic methodology. In all three demographic groups, the HQ values exhibited positively skewed distributions, indicating that while the majority of estimated exposures fall within safe limits (HQ < 1), a considerable portion--particularly those in the 95 th percentile--exceeds the threshold of concern. This finding aligns with previous studies emphasizing the importance of probabilistic approaches in capturing upper-bound exposure scenarios [25; 46]. Among the groups, children exhibited the highest HQ values, with a 95 th percentile approaching 3.45 (Figure 9). This suggests that children are at a substantially elevated risk, primarily due to their higher water intake per body weight and lower detoxification capacity. These observations are consistent with the findings of [PERSON] and [PERSON] [53], who highlighted that children are more vulnerable to environmental contaminants, particularly in regions with compromised groundwater quality. Figure 9: The nitrate HQ uncertainty and sensitivity analysis histograms for (**a**) adult males, (**b**) adult females, and (**c**) children in the pre-irrigation period. Sensitivity analysis results underscore the critical role of contaminant concentration in water (CPW), which accounts for 62-77% of the variance in HQ across all groups. This dominant influence supports the conclusion that water quality control--particularly reducing contaminant levels--is the most effective risk mitigation strategy. Water ingestion rate (AFW) also contributes significantly to the overall risk, particularly in adult females (\(\approx\)31%) and adult males (\(\approx\)27%), while exposure duration (ED) has minimal impact. These patterns corroborate earlier work by [PERSON] et al. (2018), who demonstrated that exposure variables have differing weights depending on population characteristics, but contaminant concentration consistently remains the most influential factor. As in previous simulations, the HQ values across all groups exhibit right-skewed distributions, indicating that although most simulations fall within acceptable safety limits (HQ < 1), a portion of the population may still be exposed to hazardous concentrations. Notably, the 95 th percentile values exceed 3.0 for all groups, with children again showing the highest upper bound HQ (\(\approx\)3.53), reinforcing their vulnerability ([PERSON] et al., 2018; [PERSON] et al., 2018). A critical observation from the sensitivity analysis is the shift in dominance of the input variables contributing to overall risk. While contaminant concentration in water (CPW) was the primary driver in previous analyses, in this dataset average daily water intake (AFW) becomes the most influential variable across all subgroups: \(\approx\)63% in children, 60% in adult females, and 57% in adult males (Figure 10). This suggests that the toxicological risk in this scenario is driven more by exposure behavior (volume of water consumed) than by chemical concentration. These results align with findings by WHO (2018; [PERSON] et al., 2018) and [PERSON] et al. (2018) who emphasized that ingestion rate can disproportionately affect HQ in populations with high water demand relative to body weight, such as children. Additionally, IR and ED have minimal contributions in this scenario, each accounting for less than 2% of total variance.