Time-series Mapping and Uncertainty Modeling of Environmental Variables: A Case Study of PM₁₀Concentration Mapping

Abstract

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 (PM10) concentration mapping in Incheon Metropolitan city using monthly PM10 concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series PM10 concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

Time-Series Mapping of PM10Concentration Using Multi-Gaussian Space-Time Kriging: A Case Study in the Seoul Metropolitan Area, Korea

“…To the author's knowledge, very few studies have been conducted using stochastic simulation [14] and local uncertainty assessment based on space-time kriging that does not require heavy computational cost is not fully considered. Recently, Park [17] presented a multi-Gaussian framework for time-series mapping of environmental variables. As the case study in [17] was carried out in the very small area, however, its applicability should be thoroughly investigated.…”

“…Recently, Park [17] presented a multi-Gaussian framework for time-series mapping of environmental variables. As the case study in [17] was carried out in the very small area, however, its applicability should be thoroughly investigated.…”

“…The main objective of this paper is to present spacetime kriging capable of providing uncertainty assessment information and time-series mapping of PM 10 concentrations. Within a spatial time-series framework [14,17], conventional spatial multi-Gaussian kriging is extended to a space-time domain and its potential is illustrated via a case study of monthly PM 10 concentration mapping in the Seoul metropolitan area, Korea.…”

The Adjustment of Radar Precipitation Estimation Based on the Kriging Method

“…Krajewski(1991), Chumchean et al(2006) (Wang et al, 2008). 크 리깅은 샘플 위치에서 자료값을 그대로 재생산하는 특징이 있다 (Park, 2011). 그렇기 때문에 조정된 레이 더 강우 강도의 검증을 위하여 전국의 AWS를 무작 위로 두 군집으로 나누어 각각 조정 및 검증에 사용 하였다 (Fig.…”

Geostatistical Simulation of Compositional Data Using Multiple Data Transformations

“…있고, 불확실성 모델링이 가능한 장점으로 인해 지구 과학의 많은 분야에서 활용되어 왔다 (Kyriakidis et al, 2004;Oh, 2005;Goovaerts, 2010;Park, 2010;Park, 2011). 특히 지구통계학적 시뮬레이션은 단일 위치나 동시에 여러 위치에서 고려하고 있는 속성값의 불확실성 추정에 활용될 수 있다 (Goovaerts, 1997;Deutsch and Journel, 1998;Chilès and Delfiner, 2012).…”