Research Area

Particulate matter is kinds of novel waste or pollutant in an urban atmosphere. Due to its colloidal nature, its characterization and treatment are not easily achieved until now. Further infromation is required to resolve these regarding problems. One significant lack of information on this area is no available data on the specification and source identification of particulated matter. Recently, finger-print strategy was suggested as a viable option to find out responsible source of the air pollutant. In this methodology, chemical compositions of particulate matter are analyzed periodically. Big data analysis on accumulated data and pollutant emission data from the location suspected as potential point-source is expected to representative solution. This study will be carried out in real scale study on city A, South Korea.

Two-thirds of annually manufactured antibiotics (by wt.) worldwide are consumed as veterinary antibiotics (VAs), which are used for growth promotion and medical purposes in livestock industries. With the increase of anaerobic digestion (AD) plant treating livestock manure, utilization of the AD effluent as fertilizer is causing growing concerns with the environmental exposure of VAs thorough AD. Moreover, inhibition of VAs on AD process could intensify mass loading of VAs on environmental systems by reducing its anaerobic degradation, for AD is a series of biological processes where microorganisms utilize organic matters. So far, the effects and behaviors of antibiotics in AD system remains unclear. In this study, long-term effects of antibiotics on the stability of AD system and their behavior will be elucidated through the laboratory-scale continuous flow type bioreactor.

Environmental infrastructures (e.g. landfill) are appreciably responsible for the global anthropogenic methan emission. Despite efforts to collect the gas, leakages through surface are inevitable, and its accurate estimation is crucial on the decision-making in greenhouse gas mitigation. Methods commonly used so far to estimate methane emissions from such facillities have entailed many factors to lead to inefficiency and high uncertainties. Sampling and analysis of gas at fixed point is labor-intensive and incur lots of operation cost. In this sense, a couple of novel platforms have been recently introduced. Among several tools, unmanned aerial vehicles (UAV) have and advantage that they are less limited in terms of accessibility and economically sound. In this study, both UAV-based measurement on landfill sites and analysis and simulation of equipped data with 3-dimensional advection-dispersion model are tried.

From more a decades ago, nano zero valent iron (NZVI) have been recognized as a promising option on environmental remediation, such as heavy metal adsorption from groundwater/wastewater. However, its direct use was inefficient due to the loss of reactivity: aggregation and surface oxide formation. In order to resolve the problem, use of supporting material along with its synthesis was suggested as a viable option. One problem regarding this technique is excessive use of additive chemical. In this study, use of exhausted coffee grounds, once regarded as wastes, is tried and its efficiency on NZVI supporting and application for environmental remediation are assessed.

 Recently, the use of biochar has been recognized as an environmental remediation due to its carbon sequestration effect, nutrient buffering capacity, and benefit of biomass recycles. Among many strengths, the interest in nutrient recovery capacity of biochar has increased. Since biochar can hold nutrient in several media including soil, groundwater, and wastewater, it can be used for removal of excessive nutrients in environmental media, which are severe concerns on eutrophication or drinking water quality. Given the nature of adsorption-desorption system, adsorbed nutrients then can be transferred to other media with insufficient amounts of nutrients. In this study, the potential and efficiency of nutrient recirculation by biochar made up of a variety of feedstocks.

Global climate change is increasing frequency and intensity of natural disasters. Natural disasters inevitably generate a large amount of debris in a short period of time. In South Korea, floods are the most prevalent natural disaster and are largely responsible for the generation of disaster wastes. If poorly managed, the debris can impede emergency response and lead to potential risks on public health and the environment. For proper management of flood debris, accurate and rapid estimation of debris generation is crucial. Such estimation can provide necessary information for appropriate measures on debris clean-up. Although some countries have already developed their own frameworks on debris estimation, these cannot be directly transferred to other countries because disaster events are case-dependent. Within the scopes, the aim of this study is to suggest proper methodology for development of disaster waste prediction model.