Spatial Modeling of Air Pollution Using Data Fusion

Abstract

Air pollution is a widespread issue. One approach to predicting air pollution levels in specific locations is through the development of mathematical models. Spatial models are one such category, and they can be optimized using calculation methods like the INLA (integrated nested Laplace approximation) package. It streamlines the complex computational process by combining the Laplace approximation and numerical integration to approximate the model and provides a computationally efficient alternative to traditional MCMC (Markov chain Monte Carlo) methods for Bayesian inference in complex hierarchical models. Another crucial aspect is obtaining data for this type of problem. Relying only on official or professional monitoring stations can pose challenges, so it is advisable to employ data fusion techniques and integrate data from various sensors, including amateur ones. Moreover, when modeling spatial air pollution, careful consideration should be given to factors such as the range of impact and potential obstacles that may affect a pollutant’s dispersion. This study showcases the utilization of INLA spatial modeling and data fusion to address multiple problems, such as pollution in industrial facilities and urban areas. The results show promise for resolving such problems with the proposed algorithms.