Abstract Model-estimated air pollution exposure assessments have been extensively
employed in the evaluation of health risks associated with air pollution. However, few …

Background Short-term exposure to ambient PM 2· 5 is a leading contributor to the global
burden of diseases and mortality. However, few studies have provided the global …

Abstract Machine learning models based on satellite remote sensing have gained
widespread use in estimating ground-level air pollutant concentrations, which overcome the …

Though fine particulate matter (PM 2.5) has decreased in the United States (US) in the past
two decades, the increasing frequency, duration, and severity of wildfires significantly …

Background The acute health effects of short-term (hours to days) exposure to fine
particulate matter (PM 2· 5) have been well documented; however, the global mortality …

Background: The prenatal environment influences lifetime health; epigenetic mechanisms
likely predominate. In 2016, the first international consortium paper on cigarette smoking …

As most air quality monitoring sites are in urban areas worldwide, machine learning models
may produce substantial estimation bias in rural areas when deriving spatiotemporal …

Despite the availability of numerous satellite-based machine-learning methods for
supplementing air quality monitoring data, models estimating ground-level PM 2.5 …

Accurate prediction of indoor temperature is critical for climate change adaptation and
occupant health. The aim of this study is to investigate an improved deep ensemble machine …

Short-term exposure studies have often relied on time-series of air pollution measurements
from monitoring sites. However, this approach does not capture short-term changes in …