title: Spectral estimation of global levels of atmospheric pollutants.
abstract: Underlying levels of atmospheric pollutants, assumed to be governed by smoothing mechanisms due to atmospheric dispersion, can be estimated from global emissions source databases on greenhouse gases and ozone-depleting compounds. However, spatial data may be contaminated with noise or even missing or zero-valued at many locations. Therefore, a problem that arises is how to extract the underlying smooth levels. This paper sets out a structural spatial model that assumes data evolve across a global grid constrained by second-order smoothing restrictions. The frequency-domain approach is particularly suitable for global datasets, reduces the computational burden associated with two-dimensional models and avoids cumbersome zero-inflated skewed distributions. Confidence intervals of the underlying levels are also obtained. An application to the estimation of global levels of atmospheric pollutants from anthropogenic emissions illustrates the technique which may also be useful in the analysis of other environmental datasets of similar characteristics.
authors.affiliation: Dpt of Econometrics and Statistics, University of the Basque Country, L Agirre etorb 83, E48015 Bilbao, Spain. javier.fernandezmacho@ehu.es
authors.forename: Javier
authors.initials: J
authors.lastname: Fernández-Macho
mesh_terms: D000392:Air Movements; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D001272:Atmosphere / Q000737:chemistry*; D002245:Carbon Dioxide / Q000032:analysis; D004784:Environmental Monitoring; D005583:Fourier Analysis; D057232:Global Warming; D008956:Models, Chemical
chemical_list: D000393:Air Pollutants; D002245:Carbon Dioxide