Abstract
This paper presents space-time kriging within a multi-Gaussian framework for time-series mapping of particulate matter less than 10 μm in aerodynamic diameter (PM10) concentration. To account for the spatiotemporal autocorrelation structures of monitoring data and to model the uncertainties attached to the prediction, conventional multi-Gaussian kriging is extended to the space-time domain. Multi-Gaussian space-time kriging presented in this paper is based on decomposition of the PM10concentrations into deterministic trend and stochastic residual components. The deterministic trend component is modelled and regionalized using the temporal elementary functions. For the residual component which is the main target for space-time kriging, spatiotemporal autocorrelation information is modeled and used for space-time mapping of the residual. The conditional cumulative distribution functions (ccdfs) are constructed by using the trend and residual components and space-time kriging variance. Then, the PM10concentration estimate and conditional variance are empirically obtained from the ccdfs at all locations in the study area. A case study using the monthly PM10concentrations from 2007 to 2011 in the Seoul metropolitan area, Korea, illustrates the applicability of the presented method. The presented method generated time-series PM10concentration mapping results as well as supporting information for interpretations, and led to better prediction performance, compared to conventional spatial kriging.
Highlights
Outdoor air pollution has been known as one of the risk factors that affect human health directly and/or indirectly [1,2,3,4]
Similar to the previous case study result in Park [17], these quantitative evaluation results confirmed that the incorporation of temporal autocorrelation information via space-time kriging improved the prediction performance and generated reliable mapping results for space-poor and time-rich data such as PM10 concentrations
A geostatistical approach based on spatiotemporal multiGaussian kriging was presented for time-series mapping of PM10 concentrations
Summary
Outdoor air pollution has been known as one of the risk factors that affect human health directly and/or indirectly [1,2,3,4]. In Korea, several air pollutants including particulate matter less than 10 μm in aerodynamic diameter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) have been periodically collected at several monitoring stations. Based on this real-time monitoring of air pollution, air quality levels are provided to the public domain [8]. Due to the few stations, it is very difficult to analyze the spatial characteristics and spatiotemporal dynamics of air pollutants over a wide study area during the predefined time interval [9]. Spatial interpolation or prediction is routinely applied to the sparse air pollutants observations to obtain exhaustive concentration values over the study area
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