논문 상세보기
pp.23-33
Odor complaint data collected in Seoul between 2014 and 2021 exhibited significant departures from normality. To address this, a Box-Cox transformation (l = 0.1149) was applied to stabilize variance and improve distributional normality. The transformed data were then evaluated for normality using the Shapiro-Wilk, Kolmogorov- Smirnov, and Anderson-Darling tests, all of which failed to reject the null hypothesis of normality (p > 0.05). However, the corresponding test statistics (0.989, 0.039, and 5.757, respectively) were close to their respective critical thresholds, indicating a substantial improvement in distributional normality. Based on the Box-Cox transformed dataset (l = 0.1149), the inlier range was defined as 4~190 cases/day, while observations of ≤3 cases/day and ≥191 cases/day were classified as low and high outliers, respectively. Within the inlier range, the meteorological variables exerting the strongest influence on IOCE (cases/day) were air (49.5), dew point (48.2), and surface temperature (48.1), whereas precipitation showed the weakest influence (39.7). The highest SROCE interval within the inlier range was associated with mean meteorological conditions of air (27.7±1.0oC), dew point (15.2±1.2oC) and surface temperature (30.4±1.1oC), and precipitation (0.6±1.8 mm). By contrast, the corresponding conditions in the high-outlier range were 19.6±1.2oC, 14.7±1.7oC, 23.1±1.4oC, and 0.3±0.7 mm, respectively. Across both the inlier and high-outlier ranges, the mean variation rate (VMF, %) of meteorological observations within the highest SROCE interval across the 13 meteorological factors was approximately ±11%. However, in the high-outlier range, mean meteorological conditions within complaint-concentrated intervals exhibited noticeable divergence from those observed in the inlier range. This study elucidated the meteorological drivers of IOCE and peak SROCE conditions across both inlier and high-outlier ranges. In addition to facilitating odor occurrence forecasting based on meteorological predictions, these findings provide a scientific basis for managing odor emission sources from specific facilities and regulated areas, extending beyond community level odors in residential areas.
1. 서 론
2. 연구 내용 및 방법
2.1 생활악취 민원과 기상관측 자료 수집
2.2 악취 민원 현황에서 통계적 이상값 도출
2.3 악취 민원 현황에서 SROCE와 IOCE를 활용한 기상인자의 특성 분석
3. 결과 및 고찰
3.1 Box-Cox 변환에 의한 악취 민원의 이상값 도출
3.2 악취 민원의 경계범위에서 기상 관측값의 특성
3.3 정상범위에서 민원 발생의 기상학적 특성
3.4 상한범위에서 민원 발생의 기상학적 특성
4. 결 론
References
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