AIMC Topic: Air Pollutants

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Machine learning framework for forecasting air pollution: Evaluating seasonal and climatic influences in Istanbul, Turkey.

PloS one
Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underex...

Unveiling the HONO Offsetting Effect: Rethinking NO Emission Controls during Urban Ozone Pollution Episodes.

Environmental science & technology
Conventional ozone (O) control typically targets nitrogen oxides (NO) and volatile organic compounds (VOCs), yet the role of nitrous acid (HONO) is often overlooked. Here, machine learning (ML)-derived HONO-NO reduction relationships in the real atmo...

Particle number emissions on mountainous roads: machine learning insights from on-road testing.

Environmental research
Mountainous roads pose unique challenges for controlling vehicular fine particulate number (PN) emissions, a critical pollutant impacting air quality and public health. This study integrates on-road testing with interpretable machine learning to anal...

Regional PM2.5 pollution forecasting using a hybrid model based on multi-scales feature fusion and deep learning algorithms.

PloS one
The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutan...

Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM.

PloS one
Air pollution is a global problem that threatens environmental sustainability and severely affects public health. Monitoring air quality and predicting future pollution levels are critical for creating effective environmental policies and enabling in...

Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images.

Environmental science & technology
Mobile monitoring campaigns combined with land use regression (LUR) models effectively capture fine-scale spatial variations in urban air pollution. However, traditional predictor variables often fail to capture the nuances of the built environment a...

A Novel Framework for Airshed Delineation and PM Estimation across India Using Machine Learning and Spatial Clustering.

Environmental science & technology
Air pollution continues to pose a major challenge in India, with PM being a key contributor to serious health risks. Its spatial distribution is influenced by climatic, topographic, and anthropogenic factors, which are often poorly represented in ana...

Quantifying Aviation-Related Contributions to Ambient Ultrafine Particle Number Concentrations Using Interpretable Machine Learning.

Environmental science & technology
Ultrafine particles (UFP, < 100 nm) are abundantly emitted by aircraft, but quantifying their contributions to ambient particle number concentrations (PNC) is challenging due to confounding from local traffic and complex interactions between aircraf...

Evaluation and Diagnosis of Regional Ammonia Emission Inventory in the Pearl River Delta Using Multisite NH Observations and Model Simulations.

Environmental science & technology
Ammonia (NH) has attracted increasing attention for its reduction potential in fine particulate matter mitigation, yet current NH emission inventories involve substantial uncertainties. Previous bottom-up NH inventories are usually constrained by sat...