Raman Spectral Optimization for Soot Particles: A Comparative Analysis of Fitting Models and Machine Learning Enhanced Characterization in Combustion Systems

Building and Environment, Год журнала: 2025, Номер unknown, С. 112600 - 112600

Опубликована: Янв. 1, 2025

Язык: Английский

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Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 21, 2025

Язык: Английский

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

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 8, 2025

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 various pollutants, including CO, O3, NO2, SO2, PM10, PM2.5, from 2013 2023 Tehran megacity, Iran, via deep learning (DL) models evaluate their effectiveness over conventional machine (ML) methods. Key driving variables, temperature, relative humidity, dew point, wind speed, pressure, were considered. R-squared (R2), root-mean-square error (RMSE), mean absolute (MAE), mean-square (MSE) used assess compare models. This research demonstrated that DL typically outperform ML forecasting pollution. Gated recurrent units (GRUs), fully connected neural networks (FCNNs), convolutional (CNNs) recorded R2 MSE values 0.5971 42.11 for 0.7873 171.40 0.4954 25.17 respectively. Consequently, FCNN GRU presented remarkable performance predicting NO2 (R2 = 0.6476 75.16), PM10 0.8712 45.11), PM2.5 0.9276 58.12) concentrations. In terms operational model exhibited most efficiency, with minimum maximum runtime 13 28 s, feature importance analysis suggested O3 SO2 are affected by Thus, temperature humidity primary factors affecting variability pollutant conclusions confirm achieve accuracy serve as essential instruments managing pollution, providing practical insights decision-makers adopt efficient quality control strategies.

Язык: Английский

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The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City

Atmosphere, Год журнала: 2025, Номер 16(4), С. 363 - 363

Опубликована: Март 24, 2025

This study investigated the relationship between surface meteorological variables and levels of air pollutants (O3, PM10, PM2.5) in scenarios simulated temperature increases Rio Grande, a medium-sized Brazilian city with strong industrial influence. utilized five years daily data (from 1 January 2019 to 31 December 2023) model atmospheric conditions two pollutant 21 2021 20 simulate how would respond annual °C 2 °C, employing Support Vector Machine, supervised machine learning algorithm. Predictive models were developed for both averages seasonal variations. The predictive analysis results indicated that, when considering averages, concentrations showed decreasing trend as temperatures increased. same pattern was observed scenarios, except during summer, O3 increased rise. greatest reduction occurred winter (decreasing by 10.33% 12.32% under warming respectively), while PM10 PM2.5, most significant reductions spring. lack correlation levels, along their other variables, explains Grande. research provides important contributions understanding interactions climate change, pollution, factors similar contexts.

Язык: Английский

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Assessment of the dispersion of pollutants from automobile exhaust, taking into account relative humidity, pavement temperature, wind direction and speed, which varies depending on the time of day

International Communications in Heat and Mass Transfer, Год журнала: 2024, Номер 159, С. 108140 - 108140

Опубликована: Окт. 11, 2024

Язык: Английский

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Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model

The Science of The Total Environment, Год журнала: 2024, Номер 934, С. 173193 - 173193

Опубликована: Май 12, 2024

Язык: Английский

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A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques

npj Climate and Atmospheric Science, Год журнала: 2024, Номер 7(1)

Опубликована: Дек. 19, 2024

Abstract Particulate Matter (PM) air pollution poses significant threats to public health. We introduce a novel machine learning methodology predict PM 2.5 levels at 30 m long segments along the roads and temporal scale of 10 seconds. A hybrid dataset was curated from an intensive campaign in Selly Oak, Birmingham, UK, utilizing citizen scientists low-cost instruments strategically placed static mobile settings. Spatially resolved proxy variables, meteorological parameters, properties were integrated, enabling fine-grained analysis . Calibration involved three approaches: Standard Random Forest Regression, Sensor Transferability Road Evaluations. This significantly increased spatial resolution beyond what is possible with regulatory monitoring, thereby improving exposure assessments. The findings underscore importance approaches science advancing our understanding pollution, small number participants enhancing local quality assessment for thousands residents.

Язык: Английский

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Unveiling Drivers of Zone-Specific Air Quality Predictions Using Explainable Ai: Shapley Additive Explanations-Based Insights Across Formal and Informal End-of-Life Vehicle Recycling Zones with a Green Zone Benchmark

Опубликована: Янв. 1, 2025

Язык: Английский

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Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau

Processes, Год журнала: 2025, Номер 13(5), С. 1507 - 1507

Опубликована: Май 14, 2025

To better inform the public about ambient air quality and associated health risks prevent cardiovascular chronic respiratory diseases in Macau, local government authorities apply Air Quality Index (AQI) for management within its jurisdiction. The application of AQI requires first determining sub-indices several pollutants, including respirable suspended particulates (PM10), fine (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur (SO2), carbon monoxide (CO). Accurate prediction is crucial providing early warnings to before pollution episodes occur. improve accuracy, deep learning methods such as artificial neural networks (ANNs) long short-term memory (LSTM) models were applied forecast six pollutants commonly found AQI. data this study was accessed from Macau High-Density Residential Monitoring Station (AQMS), which located an area with high traffic population density near a 24 h land border-crossing facility connecting Zhuhai Macau. novelty work lies potential enhance operational forecasting ANN LSTM run five times, average pollutant forecasts obtained each model. Results demonstrated that both accurately predicted concentrations upcoming h, PM10 CO showing highest predictive reflected Pearson Correlation Coefficient (PCC) between 0.84 0.87 Kendall’s Tau (KTC) 0.66 0.70 values low Mean Bias (MB) 0.06 0.10, Fractional (MFB) 0.09 0.11, Root Square Error (RMSE) 0.14 0.21, Absolute (MAE) 0.11 0.17. Overall, model consistently delivered PCC (0.87) KTC (0.70) lowest MB (0.06), MFB (0.09), RMSE (0.14), MAE (0.11) across all SD (0.01), indicating greater precision reliability. As result, concludes outperforms offering more accurate consistent tool management.

Язык: Английский

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An explainable prediction model for drug-induced interstitial pneumonitis

Journal of Research in Pharmacy, Год журнала: 2025, Номер 29(1), С. 322 - 334

Опубликована: Март 3, 2025

Drug-induced interstitial pneumonitis (DIP) is an inflammation of the lung interstitium, emerging due to pneumotoxic effects pharmaceuticals. The diagnosis challenging nonspecific clinical presentations and limited testing. Therefore, identifying risk drug-related required during early phases drug development. This study aims estimate DIP using binary quantitative structure-toxicity relationship (QSTR) models. dataset was composed 468 active pharmaceutical ingredients (APIs). Five critical modeling descriptors were chosen. Then, four machine-learning (ML) algorithms conducted build prediction models with selected molecular identifiers. developed validated internal 10-fold cross-validation external test set. Logistic Regression (LR) algorithm outperformed all other models, achieving 95.72% 94.68% accuracy in validation, respectively. Additionally, individual effect each descriptor on model output determined SHapley Additive exPlanations (SHAP) approach. analysis indicated that drugs might predominantly be attributed their atomic masses, polarizabilities, van der Waals volumes, surface areas, electronegativities. Apart from strong performance, SHAP local explanations can assist modifications reduce or avoid for molecule Contributing safety profile, current classification guide advanced pneumotoxicity testing late-stage failures

Язык: Английский

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