AIMC Topic: Particulate Matter
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Data-Driven Detection of Nocturnal Pollen Fragmentation Triggered by High Humidity in an Urban Environment.
Environmental science & technology
Jul 1, 2025
Biological particulate matter (BioPM) in the urban environment can affect human health and climate. Pollen, a key BioPM component, produces smaller particles when fragmented, significantly impacting public health. However, detecting pollen fragmentat...
Research on the influencing factors of PM in China at different spatial scales based on machine learning algorithm.
Environment international
Jun 1, 2025
PM pollution is one of the prominent environmental issues currently faced in China, influenced by various factors and showed significant spatial differences. In this study, the Light Gradient Boosting Machine (LightGBM) model was employed in combinat...
Exploring the link between grandmaternal air pollution exposure and Grandchild's ASD risk: A multigenerational population-based study in California.
Environment international
Jun 1, 2025
BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increasing prevalence. While genetics play a strong causal role, among environmental factors, air pollution (AP) exposure in pregnancy and infancy has been strongly endo...
Refining source-specific lung cancer risk assessment from PM-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China.
Ecotoxicology and environmental safety
Jun 1, 2025
The component-based potency factor approach, combined with benzo[a]pyrene (BaP) unit risk values from the World Health Organization (WHO), is commonly used to assess lung excess cancer risk (LECR) from polycyclic aromatic hydrocarbons (PAHs). However...
Exploring multivariate machine learning frameworks to parallelize PM simultaneous estimations across the continental United States.
Environmental pollution (Barking, Essex : 1987)
Jun 1, 2025
Fine particulate matter (PM2.5) comprises diverse chemical components, including elemental carbon (EC), silicon (SI), sulfate (SO), and calcium (CA), each linked to varied health and environmental impacts. Accurately estimating these components' spat...
Amazon's climate crossroads: analyzing air pollution and health impacts under machine learning-based temperature increase scenarios in Northern Mato Grosso, Brazil.
Environmental geochemistry and health
May 14, 2025
Air pollution has long been a public health concern in South America, now increasingly linked to climate change. In Brazil, this issue is particularly acute in smaller cities with limited monitoring infrastructure. Sinop, located in the Amazon biome ...
Interpreting hourly mass concentrations of PM chemical components with an optimal deep-learning model.
Journal of environmental sciences (China)
May 1, 2025
PM constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition co...
Long-term mortality burden trends attributed to black carbon and PM from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study.
The Lancet. Planetary health
Dec 1, 2023
BACKGROUND: Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate tr...
[Short-term effects of PM10 on cause-specific mortality and the role of long-term environmental pressures in the industrial areas of Brindisi and Civitavecchia].
Epidemiologia e prevenzione
Jan 1, 2023
OBJECTIVES: the health status of people living near industrial plants is often exposed to several environmental risk factors, including air pollution. The aim of this study is to assess the relationship between daily PM10 levels and cause-specific mo...
From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.
Proceedings of the National Academy of Sciences of the United States of America
Jun 29, 2021
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational dat...
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