Removal of nitrate and ammonium ions from livestock wastewater by hybrid systems composed of zero-valent iron and adsorbents.

The feasibility of hybrid systems for simultaneous removal of nitrate (NO3-) and ammonium ions (NH4+) from livestock wastewater was examined in batch experiments. As a part of efforts to remove nitrate and ammonium simultaneously, Fe0 and adsorbents including coconut-based granular activated carbon (GAC), sepiolite and filtralite were used. Various parameters such as adsorbent dosages and temperature were studied. Removal of NO3- increased with increase in temperature. Maximum NO3- removal (85.3%) was observed for the Fe0-filtralite hybrid system at 45 degrees C for a 24 h reaction time. Increase in GAC and sepiolite dosages had significant (P < 0.01) effect on the NH4+ removal efficiency, which was primarily due to the net negative surface charge of the adsorbents. The efficiency of hybrid systems for the removal of NO3- was in the order of filtralite > sepiolite > GAC, and the order of the removal of NH4+ was GAC > sepiolite > filtralite. The results of the present study suggest that the use of hybrid systems could be a promising innovative technology for achieving simultaneous removal of NO3- and NH4 from livestock wastewater.

Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.

The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants (both foreign-born and U.S.-born) in the U.S. above the age of 18 were invited to participate in an online survey through purposive sampling. In order to verify the variables predicting the level of psychological distress on the final sample from 42 states (n = 790), the Artificial Neural Network (ANN) analysis, which is able to examine complex non-linear interactions among variables, was conducted. The most critical predicting variables in the neural network were a person's resilience, experiences of everyday discrimination, and perception that racial discrimination toward Asians has increased in the U.S. since the beginning of the COVID-19 pandemic.