Geostatistical modelling of PM10 mass concentrations with satellite imagery from MODIS sensor

Several epidemiological studies suggested that there is an association between incidence and exacerbation of adverse respiratory and cardiovascular health effects and air pollution. Accurate, high resolution maps of ground-level Particulate Matter (PM) are highly awaited for environmental policies and future monitoring stations design. Though the measurements made by the ground stations can ensure a high level of reliability, still they cannot provide full spatial coverage over an area, giving rise among other things to misclassified epidemiological studies. Fine particles are usually categorized by size distribution, known as fractions: PM10 represents the particles with aerodynamic diameter smaller than 10 µm and comprises the thoracic (or coarse) fraction – with diameter in the range 2.5-10 µm – and the smaller inhalable (or fine) fraction. Although including the less dangerous thoracic particles, PM10 measurements are usually far more available and hence lend themselves better for modelling. Spaceborne aerosols products like the ones offered by the polar-orbiting MODerate resolution Imaging Spectrometer (MODIS) are successfully finding practical applications for scientific research studies and, though not previously intended, the Aerosol Optical Thickness (AOT, or simply τ ) from MODIS revealed to have a leading role in the evaluation of surface air quality due to its full spatial (clear-sky constrained) coverage and daily overpasses almost throughout the globe. Despite the “promised land” has not been reached yet, researchers have verified an existing correlation between aerosols and particulate concentrations, rising expectation of air quality models for high-scale environmental characterization. Air quality modelling is generally a challenging application, due to the wide range of sources affecting this variable and the high spatial and temporal variability of the particles, especially over high populated areas with rugged topography and complex meteorological profiles. In this thesis, different variogram-based geostatistical techniques are evaluated to predict the concentrations of PM10, with a focus on the effective advantages brought by AOT from satellites. This work is meant as a guide for students and researchers who are taking their first steps in this specific application, as well as to experts of the field who want to overview geostatistical filling of PM concentrations, and weigh up the usefulness of MODIS imagery. Different areas of study and temporal resolutions will be considered, so as to propose directions and outline conclusions on how this task – still far from being definitively ruled out – should be approached. Aside from modelling, the interactive visualization, extraction and analysis of the model-based predicted maps are also covered, cutting-edge Web-based software architectures based on the Open Geospatial Consortium (OGC) standard services are proposed, giving rise to increased capabilities in the spatio-temporal elaboration of the model results. The availability of spaceborne maps of AOT at an increased nominal resolution of 1×1 km2 has been a unique occasion to experiment their role for air quality issues; the latest algorithmics from leading FOSS-like (Free and Open Source Software) modelling software where learned and used, resulting in several new testing results in a field where variogram-based geostatistics were lacking. Solutions for novel online analysis and visualization capabilities were explored, in order to approach an open and interconnected uncertainty-enabled Web.