The quality of meteorological input data is essential for air pollution dispersion modeling. Traditionally, dispersion models have relied upon observational meteorological data collected from weather stations. However, the sparse national distribution of weather stations limits model ability in capturing fine-scale met...
Xue-Ying Zhang, E. Symanski, H. R. Paduch et al.· Journal of the Air and Waste...· 0 citations
Accurate high-resolution estimation of fine particulate matter (PM2.5) remains challenging because of sparse monitoring networks and missing satellite observations. We developed a multistage deep learning framework to generate daily PM2.5 concentrations at 100 m resolution across the contiguous United States (CONUS)...
A. Sheidaei, K. Gohari, Ryan Michael et al.· Environmental Science &...· 0 citations
Early life is a vulnerable period to environmental exposures that may be associated with autism spectrum disorder (ASD). We investigated associations between prenatal and postnatal exposure to weekly averages of temperature and air pollutants (PM2.5, PM10, NO2) with autism screening scores (based on the Modified Chec...
Guillaume Barbalat, A. Guilbert, L. Davidovic et al.· Environmental Science &...· 0 citations
BACKGROUND
Sudden unexpected death in infancy (SUDI) remains poorly understood, and the potential contribution of air pollution exposure is largely unexplored. Given its known impact on infant health, air pollution may influence the risk of SUDI, particularly through short-term effects.
OBJECTIVES
We aimed to evaluat...
Emmanuel Bourdet, N. Letellier, T. Benmarhnia et al.· Paediatric and Perinatal Epi...· 0 citations
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