• Clean Technology
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• v.29 no.4
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• pp.297-304
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• 2023

The rapid advancement of the high-tech electronics industry has led to a significant increase in high-concentration ammonia wastewater. Various methods have been attempted to reliably treat wastewater containing high concentrations of ammonia, but no successful technology has yet been developed and applied. In this study, the removal efficiency and characteristics of ammonia nitrogen was evaluated according to changes in temperature, air loading rate, and liquid loading rate using a closed circulation countercurrent packed tower type demonstration facility for wastewater containing high concentrations of ammonia generated in the high-tech electronics industry. The temperature was varied while maintaining operating conditions of a wastewater flowrate of 20.8 m³ h^-1 and an air flow rate of 18,000 Nm³ h^-1. The results showed that at temperatures of 45,50,55, and 60℃, the removal efficiencies of ammonia nitrogen (NH₃-N) were 87.5%, 93.4%, 96.8%, and 98.7%, respectively. It was observed that temperature had the most significant impact on the removal efficiency of NH₃-N under these conditions. As the air loading rate increases, the removal rate also increases, but the increase in removal efficiency is not significant because droplets from the absorption tower flow into the stripping tower. Even if the liquid loading rate was changed by ±30%, the removal rate did not change significantly. This does not mean that the removal rate was unaffected, but was believed to be due to the relatively high air load rate. Through demonstration research, it was confirmed that ammonia stripping is a reliable technology that can stably treat high-concentration ammonia wastewater generated in the high-tech electronics industry.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.