Objectives : Nitrogen removal processes are very important in terms of water conservation. Among them, the MLE process has been difficult to optimize because it has many variations and required experiences in operation.Methods : In this work, we quantitatively analyzed the nitrification of the MLE process using the STOAT simulation program. In particular, we attempted to improve nitrification rate even at lower water temperatures.Results and Discussion : As a result, more than 93% ammonia was nitrificated when the water temperature was above 20℃, and a lower reduction rate of ammonia was observed when the temperature was below 15℃. Simulations applying three process variables (MLSS, DO concentration, and RAS) were carried out once or several times to increase nitrogen removal efficiency at 10℃, and the most efficient variable was ‘RAS increase’(55% reduction of ammonia).Conclusions : For more efficient nitrification rate, simultaneous increases in RAS and DO were required. In this case, the ammonia concentration in the effluent dropped by 61.4% and it was desirable to increase the MLSS return volume for T-N concentration reduction.
Significant variation in the precipitation events caused by global climate change has made it difficult to manage water resources due to the increased frequency of unexpected droughts and floods. Under these conditions, groundwater is needed to ensure a sustainable water supply; thus, estimates of precipitation recharge are essential. In this study, we derived an apparent recharge coefficient (ARC) from a modified water table fluctuation equation to predict groundwater storage changes due to precipitation events. The ARC is calculated as the ratio of the recharge rate over the specific yield (R/Sy); therefore, it implicitly expresses variation in Sy. The ARC varies spatially and temporally, corresponding to the precipitation events and hydrogeological characteristics of unsaturated materials. ARCs for five monitoring wells from two basins in Korea in different seasons were calculated using a 10-year groundwater level and weather dataset for 2005–2014. Then, the reliability of the ARCs was tested by the comparison of the predicted groundwater level changes for 2015 and 2016 with observed data. The root mean square error ranged from 0.03 to 0.09 m, indicating that the predictions were acceptable, except for one well, which had thick clay layers atop the soil layer; the low permeability of the clay slowed the precipitation recharge, interfering with groundwater level responses. We performed a back-calculation of R from the Sy values of the study areas; the results were similar to those obtained via other methods, confirming the practical applicability of the ARC. In conclusion, the ARC is a viable method for predicting groundwater storage changes for regions where long-term monitoring data are available, and subsequently will facilitate advanced decision making for allocating and developing water resources for residents, industry, and groundwater-dependent ecosystems.
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