Air pollution represents a significant public health problem, but the uneven distribution of meteorological stations and the omission of the spatio-temporal dependence of pollutants makes it difficult to make accurate estimations of their actual exposure in the population. The proposed solution uses an optimised Bayesian predictive model that takes these issues into account, and provides more accurate predictions of air pollution levels, with an associated low computational cost.
This technology is statistical code-based system that consists of a hierarchical Bayesian spatiotemporal model, which predicts the short- and long-term exposure to air pollutants and meteorological variables, using stochastic partial differential equations (SPDEs) of the integrated Laplace approximation (INLA). Those predictions are very accurate and efficient, with much lower computational time and low density of monitoring stations.
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The University of Girona (UdG) by means of its Research & Technology Transfer Office (TTO) provides services to 117 research groups, 42 of which have been recognized as Consolidated Research Group of Catalonia and 4 among them have been incorporated into the TECNIO network, the top Catalonia's Government excellence research network.
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