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Particulate Matter

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Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning.

Environmental geochemistry and health
The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM, SO, NO, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological condi...

Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.

Environment international
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....

Machine learning helps reveal key factors affecting tire wear particulate matter emissions.

Environment international
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wea...

Mapping of high-resolution daily particulate matter (PM) concentration at the city level through a machine learning-based downscaling approach.

Environmental monitoring and assessment
PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is crucial for assessing air quality. However, Vietnam's state-of-the-art (SOTA) dataset with a 3 km r...

Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The Science of the total environment
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...

Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM: Implications for Assessment of Health Impacts.

Environmental science & technology
Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wil...

Particulate matter estimation using satellite datasets: a machine learning approach.

Environmental science and pollution research international
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...

Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Scientific reports
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO,...

The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.

The Science of the total environment
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM)...