2005, Vol.14, No.4

Journal of Environmental Impact Assessment 2023 KCI Impact Factor : 0.51

pISSN : 1225-7184
https://journal.kci.go.kr/kseia

This study was designed to investigate the effect of plant as a botanical air purification on theindoor pollution by formaldehyde. Three indoor plants such as Dracaena marhginata,Spathiphyllum and Dracaena reflexa, were placed in the artificially contaminated reactor underlaboratory condition. Both plant and soil effects on removal of formaldehyde from contaminatedindoor air were observed. Reductions in the formaldehyde levels appeared to have beenassociated with soil medium factors as well as plant factors. The effect of soil on formaldehydereduction was high in the early stage of the experiment and the results suggest that sorptioncould be more important factor than microbial degradation in the initial dissipation ofcontaminants in the soil. It was suggested that the effect of plant on formaldehyde reductionmight be related to the plant species, total leaf surface area of plant, degree of contribution of soilmedium, and exposed concentration level. The results of this study showed that air purificationusing plants is an effective means of reduction on indoor formaldehyde level, though, utilizationof soil media with high sorption capacity and/or supplementary purifying aids were alsosuggested when the source is continuous or exposed concentration level is high.

Existing models that predict of Daily water supply include statistical models and neuralnetwork model. The neural network model was more effective than the statistical models. Onlyneural network model, which predict of Daily water supply, is focused on estimation of theoperational control. Neural network model takes long learning time and gets into localminimum.This study proposes Neuro Genetic hybrid model which a combination of genetic algorithmand neural network. Hybrid model makes up for neural network’s shortcomings. In this study,the amount of supply, the mean temperature and the population of the area supplied with waterare use for neural network’s learning patterns for prediction.RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). Thecomparison of the two models showed that the predicting capability of Hybrid model is moreeffective than that of neural network model. The proposed hybrid model is able to predict ofDaily water, thus it can apply real time estimation of operational control of water works andwater drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

This study was carried out to offer the basic methodology of the system and model toobjectively assess the natural ecosystem for environmentally friendly land conservation andpresent the alternative plan on establishing the environmental policy. The results of this studywere as follows.We selected four assessment factors associated with biotic, abiotic, qualitative, and functionalfactors. Also, there were extracted fifty-six indicators including density, total nitrogen, hemerobydegree, and goods production. The assessment factor showed that biotic one was veryimportant. The importance of indicators were analyzed that rare and endangered plant wasimportant in biotic factor, in case of abiotic, qualitative, and functional factors, organic matter,landscape diversity, and conservation of ecosystem were greatly important. The results of factoranalysis on the characteristics of indicators, classified biotic factor into six factors including astructural one, abiotic factor as a soil and physical one, qualitative factor as five ones includinghierarchical one, and functional factor as public and conservational one. In the results of analysis on assessment model, R-square of biotic factor was 51.7%, those of abiotic, qualitative, and functional one were each 58.4%, 44.2%, and 39.3%, and statistical problem was no existence. In future, to develop the assessement model and methodology of sustainable natural ecosystem, we will have to achieve the integrated model and grouping by assessment factor.