AIMC Topic: Particulate Matter

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Association between air pollution and type II diabetes in Italy from clinical data and population-weighted exposure at the municipality level.

Scientific reports
A growing body of literature supports the association between ambient particulate pollution and the risk of type 2 diabetes (T2DM). Both issues are particularly relevant in Italy. This study investigates the relationship between T2DM and exposure to ...

Familial Differences in Personal PM Exposure within a Rural African Community Explained with Spatiotemporal Exposure Apportionment.

Environmental science & technology
Exposure to fine particulate matter (PM) from solid-fuel combustion is a major determinant of global morbidity and mortality. However, variations in exposure remain uncertain across many high-risk populations. This work describes personal PM exposure...

Fusing satellite imagery and ground-based observations for PM air pollution modeling in Iran using a deep learning approach.

Scientific reports
With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges and issues in many countries. The concentration of particulate matter with an aerodynamic diameter of...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

Environmental health perspectives
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...

Long-term exposure to PM and liver cancer mortality: Insights into the role of smaller particulate fractions.

Ecotoxicology and environmental safety
Particulate matter (PM) is a recognized carcinogen, but the effects of PM on liver cancer remain underexplored. This study investigates the long-term association between PM and liver cancer mortality, as well as the contribution of smaller particles ...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM in China.

Frontiers in public health
OBJECTIVES: This study investigated association between long-term PM exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM with time lags and build a prediction model using machine l...

Low-Cost Particulate Matter Mass Sensors: Review of the Status, Challenges, and Opportunities for Single-Instrument and Network Calibration.

ACS sensors
As an emerging atmospheric monitoring technology, low-cost sensors for particulate matter of diameters below 2.5 μm (PMLCSs) supplement traditional air quality monitoring instruments. Because their stability and accuracy are typically low, they requi...

Machine learning-based quantification and separation of emissions and meteorological effects on PM in Greater Bangkok.

Scientific reports
This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM over Greater Bangkok (G...