This study combined a three-year time series of air pollutant measurements from the
Michigan Air Sampling Network (MASN) with spatially detailed datasets for two two-week …

PM 10 is one of the most important environmental problems in urban areas that may have a
significant impact on human health. Therefore, forecasting is an important tool for warning …

The concentration of a pollutant in the atmosphere is a random variable that cannot be
predicted accurately, but can be described using quantities such as ensemble mean …

A method based on a statistical approach of estimating uncertainty in simulating the
transport and dispersion of atmospheric pollutants is developed using observations and …

In the last two decades, increasing attention has been given to air pollution around the
world, mainly because of its impact on human health and on the environment. In the Po …

An advanced computational procedure is presented for the source apportionment (SA) of
airborne particulate matter (PM) using chemical mass balance (CMB) receptor modeling …

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which
presents challenges to estimating exposures for health effects assessment. Here we created …

This paper presents space‐time kriging within a multi‐Gaussian framework for time‐series
mapping of particulate matter less than 10 μm in aerodynamic diameter (PM10) …

Bayesian statistical methods are used to evaluate Community Multiscale Air Quality (CMAQ)
model simulations of sulfate aerosol over a section of the eastern US for 4-week periods in …

This introductory scholarly treatment explores the fundamentals of modern geostatistics, a
group of spatiotemporal concepts and methods related to the advancement of the epistemic …