Volatile organic compounds (VOCs) are the precursors of forming ozone (O 3) and fine
particulate matter (PM 2.5). Accurate estimates of biogenic VOC (BVOC) emissions is …

The limited number of ozone monitoring stations imposes uncertainty in various
applications, calling for accurate approaches to capturing ozone values in all regions …

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for
the scientific management of atmospheric pollution. This study develops a model that …

In this study, we leverage multiple linear regression and quantile regression combined with
a novel deep learning tool (SHapley Additive exPlanations) to isolate the impact of …

In response to the need for an up-to-date emissions inventory and the recent achievement of
geostationary observations afforded by the Geostationary Environment Monitoring …

In this study, we introduce a deep learning-based framework, Deep-BCSI, which leverages
Convolutional Neural Networks (CNN) for bias correction and Partial Convolutional Neural …

This study investigates the joint effect of air pollution and different types of green spaces (eg
mixed forests) on stress levels in South Korea. Two periods were examined: before the …

To quantitatively investigate the transboundary behaviors and source attributions of ozone
(O3) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ …

High concentrations of pollutants in the atmosphere negatively impacts public health and
other domains. Although surface measurements of pollutants at ground stations are quite …

Pioneering the use of the Geostationary Environment Monitoring Spectrometer's (GEMS)
observation data in air quality modeling, we updated Asia's NO x emissions inventory by …