Environmental Pollution Damage: Analysis of Recovery in Jangjeom Village, Korea

• 영문명: Environmental Pollution Damage: Analysis of Recovery in Jangjeom Village, Korea
• 발행기관: 인간식물환경학회
• 저자명: Jae-hyuck Lee Do-kyun Kim
• 간행물 정보: 『인간식물환경학회지(JPPE)』제25권 제5호, 499~511쪽, 전체 13쪽
• 주제분류: 자연과학 > 자연과학일반
• 파일형태: PDF
• 발행일자: 2022.10.31

영문 초록

Background and objective: Since industrialization, various communities worldwide have had to deal with the negative effects of pollution. Previous studies have investigated how communities affected by pollution can recover. However, few studies have explored the ways in which recovery takes place over time. This study examined the restoration process of the village of Jangjeom in Iksan, South Korea. The environmental contamination of the air and soil of this area, caused by a fertilizer factory, has been officially recognized. As such, its regional administration is planning its recovery with governmental support. Methods: This study conducted a content analysis of the meeting minutes of the government-civilian committee on environmental emergency measures in the village, using time-series text mining. The main phases and problems in the process of environmental pollution recovery were analyzed. Results: The results of the analysis show that, in the early days of this initiative, there were many discussions regarding the physical and environmental aspects of pollution and the legal framework required to investigate the sources of pollution. However, in later meetings, the focus shifted to how pollution affects the residents, covering topics such as residents’ health check-ups and consultations with experts and administrative agencies. Conclusion: This study shows that for the long-term recovery of communities damaged by pollution, a human-centered approach that goes beyond the physical and environmental aspects of recovery is needed.

목차

Introduction

Research Methods

Results and Discussion

Conclusion

References

키워드

contaminated communities fertilizer factory government-civilian committee Iksan time-series text mining