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Recent times have seen an exponential increase in the amount of spatial data, whichis in many cases associated with temporal data. Recent advances in computer technol-ogy and computation of hierarchical Bayesian models have enabled to analyze complexspatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide(NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. Weconsidered an independent Gaussian process model and an auto-regressive model andcarried out estimation within a hierarchical Bayesian framework with Markov chainMonte Carlo techniques. A Gaussian predictive process approximation has shown thebetter prediction performance rather than a Hierarchical auto-regressive model for theillustrative NO2 concentration levels at any unmonitored location.Keywords: Auto-regressive model, bayesian inference, gaussian predictive process, ni-trogen dioxide, space-time modelling. 1. Introduction Air pollutants are well known to have a negative impact on human health and they havetherefore been studied by many researchers. In Korea, ve air pollutants are monitored andmanaged by the government, which are particulate matter10 (PM10), ozone (O3), nitrogendioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). This study concerns thespatio-temporal characteristics of NO2, which a ects respiratory diseases (Choi et al., 2000)and leads to the formation of photochemical smog.The main source of NO2 in Korea is the burning of fossil fuels, such as coal, oil, and gas.In urban areas, most of the NO2 is generated by motor vehicle exhaust emissions (around80%). Other sources of NO2 are petrol combustion and the vaporization of metal particles,coal- red power stations for electricity generation, other manufacturing industries, and theheating of boilers in residential environments. Hence, in the winter season, NO2 levels arehigh because of the necessity to produce heat.

Spatio-temporal models for generating a map of high resolution NO2 level