Mortality attributable to long-term exposure to ambient fine particulate matter: insights from the epidemiologic evidence for understudied locations

KJ Colonna, P Koutrakis, PL Kinney… - Environmental …, 2022 - ACS Publications
Epidemiologic cohort studies have consistently demonstrated that long-term exposure to
ambient fine particles (PM2. 5) is associated with mortality. Nevertheless, extrapolating …

[HTML][HTML] Federated learning meets remote sensing

S Moreno-Álvarez, ME Paoletti… - Expert Systems with …, 2024 - Elsevier
Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …

EAACI guidelines on environmental science in allergic diseases and asthma–leveraging artificial intelligence and machine learning to develop a causality model in …

MH Shamji, M Ollert, IM Adcock, O Bennett, A Favaro… - Allergy, 2023 - Wiley Online Library
Allergic diseases and asthma are intrinsically linked to the environment we live in and to
patterns of exposure. The integrated approach to understanding the effects of exposures on …

Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods

Y Zhang, K Shi, X Sun, Y Zhang, N Li… - GIScience & Remote …, 2022 - Taylor & Francis
Secchi disk depth (SDD) is a simple but particularly important indicator for characterizing the
overall water quality status and assessing the long-term dynamics of water quality for …

Spatial resolved surface ozone with Urban and rural differentiation during 1990–2019: a space–time bayesian neural network downscaler

H Sun, YM Shin, M Xia, S Ke, M Wan… - Environmental …, 2021 - ACS Publications
Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and
associated premature deaths, and thus population-level environmental health studies …

[HTML][HTML] Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

H Tao, AH Jawad, AH Shather, Z Al-Khafaji… - Environment …, 2023 - Elsevier
This study uses machine learning (ML) models for a high-resolution prediction (0.1°× 0.1°) of
air fine particular matter (PM 2.5) concentration, the most harmful to human health, from …

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

AP Rudke, JA Martins, R Hallak, LD Martins… - Remote Sensing of …, 2023 - Elsevier
Atmospheric pollutant data retrieved through satellite sensors are continually used to assess
changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several …

Long-term effects of PM2. 5 components on incident dementia in the northeastern United States

J Li, Y Wang, K Steenland, P Liu, A van Donkelaar… - The Innovation, 2022 - cell.com
Growing evidence has linked long-term fine particulate matter (PM 2.5) exposure to
neurological disorders. Less is known about the individual effects of PM 2.5 components. A …

Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization

I Ahmadianfar, S Shirvani-Hosseini… - … Science and Pollution …, 2022 - Springer
Undeniably, there is a link between water resources and people's lives and, consequently,
economic development, which makes them vital in health and the environment. Proper water …

Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

L Lin, Y Liang, L Liu, Y Zhang, D Xie, F Yin, T Ashraf - Remote Sensing, 2022 - mdpi.com
Fine particulate matter (PM2. 5) is a major pollutant in Guanzhong Urban Agglomeration
(GUA) during the winter, and GUA is one of China's regions with the highest concentrations …