[HTML][HTML] A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States

L Li, AJ Blomberg, J Lawrence, WJ Réquia, Y Wei… - Environment …, 2021 - Elsevier
Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the
abundance of radionuclides that are bound to airborne particulates. Exposure to high levels …

Exposure to particle Beta radiation in greater Massachusetts and factors influencing its spatial and temporal variability

AJ Blomberg, L Li, JD Schwartz, BA Coull… - … science & technology, 2020 - ACS Publications
Particle radioactivity is a property of airborne particles caused by the presence of naturally
occurring or anthropogenic radionuclides. Recent studies have found associations between …

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

H Lee, HA Hanson, J Logan, D Maguire… - Environmental …, 2024 - Springer
Characterizing the interplay between exposures shaping the human exposome is vital for
uncovering the etiology of complex diseases. For example, cancer risk is modified by a …

Predicting monthly community-level domestic radon concentrations in the greater Boston area with an ensemble learning model

L Li, AJ Blomberg, RA Stern, CM Kang… - Environmental …, 2021 - ACS Publications
Inhaling radon and its progeny is associated with adverse health outcomes. However,
previous studies of the health effects of residential exposure to radon in the United States …

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States

BS Beckerman, M Jerrett, M Serre… - … science & technology, 2013 - ACS Publications
Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which
presents challenges to estimating exposures for health effects assessment. Here we created …

[HTML][HTML] A high resolution spatiotemporal fine particulate matter exposure assessment model for the contiguous United States

C Brokamp - Environmental Advances, 2022 - Elsevier
Currently available nationwide prediction models for fine particulate matter (PM 2.5) lack
prediction confidence intervals and usually do not describe cross validated model …

Model prediction of radioactivity levels in the environment and food around the world's first AP 1000 nuclear power unit

P Wang, W Huang, H Zou, X Lou, H Ren, S Yu… - Frontiers in Public …, 2024 - frontiersin.org
Objectives Model prediction of radioactivity levels around nuclear facilities is a useful tool for
assessing human health risks and environmental impacts. We aim to develop a model for …

Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2. 5) using satellite data over large …

AC Just, KB Arfer, J Rush, M Dorman, A Shtein… - Atmospheric …, 2020 - Elsevier
Reconstructing the distribution of fine particulate matter (PM 2.5) in space and time, even far
from ground monitoring sites, is an important exposure science contribution to epidemiologic …

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States

L Li, RA Stern, E Garshick, CL Zilli Vieira… - Environmental …, 2023 - ACS Publications
In 1987, the United States Environmental Protection Agency recommended installing a
mitigation system when the indoor concentration of radon, a well-known carcinogenic …

[HTML][HTML] Space-time trends of PM2. 5 constituents in the conterminous United States estimated by a machine learning approach, 2005–2015

X Meng, JL Hand, BA Schichtel, Y Liu - Environment international, 2018 - Elsevier
Particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5) is a complex mixture
of chemical constituents emitted from various emission sources or through secondary …