AIMC Topic: Particulate Matter

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Beyond model-specific biases: An explainable multifaceted approach for robust PM source apportionment.

Environmental research
Liu et al. (2025) present an innovative approach to PM source apportionment in urban environments by integrating Positive Matrix Factorization with machine learning (ML) models including XGBoost, Random Forest (RF), and Support Vector Machine (SVM). ...

Quantifying role of source variations on PM-bound toxic components under climate change: Measurement at multiple sites during 2018-2022 in a Chinese megacity.

Journal of hazardous materials
Understanding the response of PM-bound toxic components to source variations under climate change is crucial for public health protection. However, the lack of long-term and multi-site observational data of toxic components limits such efforts. Here,...

Air quality monitoring and mitigation through time series forecasting and stochastic optimization.

Journal of environmental management
Poor air quality poses significant threats to public health and environmental sustainability. To mitigate such risks, accurate air quality prediction is essential to inform intervention policies that effectively reduce pollutant levels. While past re...

Revolutionizing Satellite Real-Time Air Pollution Alerts through New On-Orbit System-on-Chip Technology.

Environmental science & technology
Exposure to abnormally high concentrations of particulate matter and ozone can cause severe harm to human health, highlighting the need for real-time satellite monitoring to enable rapid responses and timely warnings. However, the existing methods fo...

Quantifying regional transport contributions to PM-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach.

Environmental pollution (Barking, Essex : 1987)
Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. This study analyzed PM-bound trace elements based on one year of sampling data collected from a low-PM...

Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018).

Environment international
BACKGROUND: Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over several decades, with environmental factors like air pollution (AP) increasingly implicated. This study investigates associations between prenatal exposu...

Non-traditional socio-environmental and geospatial determinants of Alzheimer's disease-related dementia mortality.

The Science of the total environment
IMPORTANCE: Recent data point to the impact of non-traditional environmental and social factors on Alzheimer's Disease-Related Dementias (ADRD) mortality. Our study aimed to determine the extent to which antecedent air pollution, social vulnerability...

Revealing the impacts of the built environment factors on pedestrian-weighted air pollutant concentration using automated and interpretable machine learning.

Journal of environmental management
Urban air pollution poses significant health risks, especially to pedestrians due to their proximity to pollutants and lack of physical protection. Understanding the influence of built environment factors is essential to mitigate this pollution and s...

Data-Driven Detection of Nocturnal Pollen Fragmentation Triggered by High Humidity in an Urban Environment.

Environmental science & technology
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
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...