Integrating Artificial Intelligence into Causal Research in Epidemiology

Current Epidemiology Reports, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 24, 2025

Language: Английский

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Understanding the spatial and seasonal variation of the ground-level ozone in Southeast China with an interpretable machine learning and multi-source remote sensing

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 917, P. 170570 - 170570

Published: Jan. 29, 2024

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Quantitative Analysis of Multi-Elements in a Micron-Sized Single Particle Based on Laser-Induced Breakdown Spectroscopy Signal Enhancement of an Optical Fiber Collimated System

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

With rapid, energy-intensive, and coal-fueled economic growth, global air quality is deteriorating, particulate matter pollution has emerged as one of the major public health problems worldwide. It extremely urgent to achieve carbon emission reduction prevention control, aiming at common problem weak unstable signals characteristic elements in application laser-induced breakdown spectroscopy (LIBS) technology for trace element detection. In this study, influence optical fiber collimation signal enhancement method on LIBS was explored. Then, system based an collimated spectral intensity signal-to-noise ratio (SNR) compared, influences different preprocessing methods variable selection prediction performance random forest (RF) calibration model were investigated. Finally, Savitzky–Golay convolution derivative (SG)-variable importance projection (VIP)-mutual information (MI)-RF (Zn), first-order (D1st)-variable measurement (VIM)-successive projections algorithm (SPA)-RF (Cu), D1st-VIM-MI-RF (Ni) optimal models constructed according hybrid method. The performances their RF after SG-VIP-MI D1st-VIM-SPA D1st-VIM-MI are presented follows: Zn (Rp2 = 0.9860; MREP 0.0590), Cu 0.9817; 0.0405), Ni 0.9856; 0.0875). above results demonstrate that method, strategy overcome key low SNR quantitative accuracy single particle expected provide a theoretical basis technical support situ online rapid monitoring matter.

Language: Английский

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Advanced Deep Learning Model for Predicting Water Pollutants Using Spectral Data and Augmentation Techniques: A Case Study of the Middle and Lower Yangtze River, China

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107058 - 107058

Published: March 1, 2025

Language: Английский

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Machine learning analysis of drivers of differences in PAH content between PM1 and PM10 in Zagreb, Croatia

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102541 - 102541

Published: April 1, 2025

Language: Английский

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Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution

ACS ES&T Air, Journal Year: 2025, Volume and Issue: unknown

Published: April 15, 2025

Language: Английский

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AI and Machine Learning for Optimizing Waste Management and Reducing Air Pollution

Journal of Hazardous Toxic and Radioactive Waste, Journal Year: 2025, Volume and Issue: 29(3)

Published: April 21, 2025

Language: Английский

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Exploring the Influencing Factors of Surface Ozone Variability by Explainable Machine Learning: A Case Study in the Basilicata Region (Southern Italy)

Atmosphere, Journal Year: 2025, Volume and Issue: 16(5), P. 491 - 491

Published: April 24, 2025

Exposure to high surface ozone (O3) concentrations, which is a major air pollutant and greenhouse gas, constitutes significant public health concern, especially considering the potential adverse impact of climate change on future O3 values. The implementation increasingly effective methods assess factors determining formation variability is, therefore, great significance. In this study, methodological approach combining both supervised unsupervised machine learning algorithms (MLAs) with Shapley additive explanations (SHAP) method was used understand key behind explore nonlinear relationships linking these factors. SHAP analysis carried out at different event scales indicated (i) dominant role meteorological variables in driving variability, mainly relative humidity, wind speed, temperature throughout study period; (ii) an increase contribution temperature, nitrogen oxides, carbon monoxide concentrations during selected pollution event; (iii) predominant effect speed humidity shaping daily patterns clustered using k-means technique. results obtained are expected be useful for definition measures prevent and/or mitigate damage associated exposure.

Language: Английский

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Combined PMF modelling and machine learning to identify sources and meteorological influencers of volatile organic compound pollution in an industrial city in eastern China

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 334, P. 120714 - 120714

Published: July 23, 2024

Language: Английский

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Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

To further reduce atmospheric particulate matter concentrations, there is a need for more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resolution imaging detailed compositional analysis particles with relatively stable physical chemical properties. This study introduces an advanced source apportionment pipeline (RX model) that uniquely combines computer-controlled scanning computer vision machine learning to trace particle sources by integrating single-particle morphology, size, information. In the evaluation using virtual data set known contributions, RX model demonstrated high accuracy, average errors 0.60% number 1.97% mass contribution. Compared balance model, model's accuracy stability improved 75.6 73.4%, respectively, proved effective in tracing Fe-containing atmosphere steel city China. indicates morphology serve as feature determining its source. findings highlight potential coupled techniques enhance our understanding pollution sources, offering valuable insights PM health risk assessment evidence-based policy-making.

Language: Английский

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