Add to ORKGIn this paper, we discuss the dynamic coregionalization model and its capability for model selection inference and interpretation in relation to spatio- temporal dynamic calibration and mapping of daily concentration of airborne particulate matter. To do this, we consider the problem of joint modelling ground level concentration data and satellite measurements of aerosol optical thickness (AOT), which are rarely available. The maximum likelihood estimation for the large data set related to the ”padano-veneto” region, North Italy, with missing data is covered by the stable EM algorithm and implemented on a small size computer cluster
Mapping the mass concentration of near-surface atmospheric particulate matter (PM) using satellite o...
Particulate matter (PM) is one of the most critical air pollutants because of its effects on the hum...
There has been growing interest in extending the coverage of ground particulate matter with aerodyna...
AURA, produce data for the concentration of various airborne pollutants. Calibrat-ing satellite data...
The satellites from NASA's Earth Science Project Division, like AURA, produce data for the concentra...
Multivariate spatio-temporal statistical models are deserving for increasing attention for environme...
This paper introduces a flexible space-time data fusion model based on latent variables and varying ...
In the last decades, air quality monitoring networks have been increasingly installed around the wor...
The PM10 concentrations are often measured by different instruments situated in different sites. As ...
In this work we propose the multivariate extension of a spatio-temporal model known in the literatur...
Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground...
Abstract: Daily monitoring of unhealthy particles suspended in the low troposphere is of major conc...
One major role of environment agencies is to provide concise indicators about a country's air qualit...
There is considerable demand for accurate air quality information in human health analyses. The spar...
We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by ...
Mapping the mass concentration of near-surface atmospheric particulate matter (PM) using satellite o...
Particulate matter (PM) is one of the most critical air pollutants because of its effects on the hum...
There has been growing interest in extending the coverage of ground particulate matter with aerodyna...
AURA, produce data for the concentration of various airborne pollutants. Calibrat-ing satellite data...
The satellites from NASA's Earth Science Project Division, like AURA, produce data for the concentra...
Multivariate spatio-temporal statistical models are deserving for increasing attention for environme...
This paper introduces a flexible space-time data fusion model based on latent variables and varying ...
In the last decades, air quality monitoring networks have been increasingly installed around the wor...
The PM10 concentrations are often measured by different instruments situated in different sites. As ...
In this work we propose the multivariate extension of a spatio-temporal model known in the literatur...
Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground...
Abstract: Daily monitoring of unhealthy particles suspended in the low troposphere is of major conc...
One major role of environment agencies is to provide concise indicators about a country's air qualit...
There is considerable demand for accurate air quality information in human health analyses. The spar...
We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by ...
Mapping the mass concentration of near-surface atmospheric particulate matter (PM) using satellite o...
Particulate matter (PM) is one of the most critical air pollutants because of its effects on the hum...
There has been growing interest in extending the coverage of ground particulate matter with aerodyna...