Space-time data analysis and assimilation techniques in atmospheric sciences typically
consider input from monitoring measurements. The input is often processed in a manner that …

This study focuses on modeling air quality with an adaptive utilization of spatio-temporal
information from multiple air quality monitoring stations under a multi-scale framework. To …

Artificial Intelligence has the potential to contribute to sustainable cities, life on land, and
climate action. Specifically, data-driven AI models can analyze large, interconnected …

Air pollution is an important problem for public health. The spatiotemporal analysis is a
crucial step for understanding the complex characteristics of air pollution. Using many …

Air pollution seriously affects public health, while effective air quality prediction remains a
challenging problem since the complex spatial-temporal couplings exist in multi-area …

We provide methods that can be used to obtain more accurate environmental exposure
assessment. In particular, we propose two modeling approaches to combine monitoring data …

Accurate identification of distant, large, and frequent sources of emission in cities is a
complex procedure due to the presence of large-sized pollutants and the existence of many …

This paper is concerned with the spatiotemporal mapping of monthly 8-h average ozone
(O3) concentrations over California during a 15-years period. The basic methodology of our …

States in the USA are required to demonstrate future compliance of criteria air pollutant
standards by using both air quality monitors and model outputs. In the case of ozone, the …

This work is concerned with the composite space/time analysis of ozone concentrations over
Eastern US A novel method is used, which introduces a mode of reasoning that is a …