Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model

Environmental Research, Journal Year: 2023, Volume and Issue: 229, P. 115775 - 115775

Published: April 6, 2023

Language: Английский

---

Influence and prediction of PM2.5 through multiple environmental variables in China

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 849, P. 157910 - 157910

Published: Aug. 6, 2022

Language: Английский

---

Optimizing modeling windows to better capture the long-term variation of PM2.5 concentrations in China during 2005–2019

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 854, P. 158624 - 158624

Published: Sept. 8, 2022

Language: Английский

---

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

Environment International, Journal Year: 2023, Volume and Issue: 175, P. 107931 - 107931

Published: April 15, 2023

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered area implement method. Different lags changing patterns four European Reanalysis (ERA5) variables, rainfall, mean temperature, wind speed relative humidity, one parameter, moisture, were used select suitable set predictors using non-greedy algorithm known as simulated annealing (SA). The selected simulate temporal spatial variability PM2.5 concentration over during early summer (May-July), polluted months, three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) long short-term memory (LSTM) integrated with Bayesian optimizer. distribution annual average revealed population whole is exposed pollution level above standard limit. changes in temperature moisture humidity month before can predict May-July. Results higher performance LSTM normalized root-mean-square error Kling-Gupta efficiency 13.4% 0.89, compared 16.02% 0.81 SDG-BP 17.9% 0.74 ERT. could also reconstruct observed MapCurve Cramer's V values 0.95 0.91, 0.9 0.86 SGD-BP 0.83 0.76 provided methodology forecasting at high resolution peak months freely available data, which be replicated other regions generating maps.

Language: Английский

---

Clarifying Relationship between PM2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(2), P. 358 - 358

Published: Jan. 6, 2023

Atmospheric fine particles (PM2.5) have been found to be harmful the environment and human health. Recently, remote sensing technology machine learning models used monitor PM2.5 concentrations. Partial dependence plots (PDP) were explore meteorology mechanisms between predictor variables concentration in “black box” models. However, there are two key shortcomings original PDP. (1) it calculates marginal effect of feature(s) on predicted outcome a model, therefore some local effects might hidden. (2) requires that for which partial is computed not correlated with other features, otherwise estimated feature has great bias. In this study, PDP’s analyzed. Results show contradictory correlation temperature can given by Furthermore, spatiotemporal heterogeneity PM2.5-AOD relationship cannot displayed well The drawbacks PDP make unsuitable exploring large-area effects. To resolve above issue, multi-way recommended, characterize how concentrations changed temporal spatial variations major meteorological factors China.

Language: Английский

---

Influence of multivalent background ions competition adsorption on the adsorption behavior of azo dye molecules and removal mechanism: Based on machine learning, DFT and experiments

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 341, P. 126810 - 126810

Published: Feb. 19, 2024

Language: Английский

---

Time-Series Data-Driven PM2.5 Forecasting: From Theoretical Framework to Empirical Analysis

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 292 - 292

Published: Feb. 28, 2025

PM2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need develop accurate prediction models support decision-making reduce risks. This review comprehensively explores progress of concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, future development directions. article obtained on 2327 journal articles published from 2014 2024 WOS database. Bibliometric analysis shows that research output growing rapidly, with China United States playing leading role, recent increasingly focusing data-driven methods such as learning. Key sources include ground monitoring, meteorological observations, remote sensing, socioeconomic activity data. Deep (including CNN, RNN, LSTM, Transformer) perform well capturing complex temporal dependencies. With its self-attention mechanism parallel processing capabilities, Transformer particularly outstanding addressing challenges long sequence modeling. Despite these advances, integration, model interpretability, computational cost remain. Emerging technologies meta-learning, graph neural networks, multi-scale modeling offer promising solutions while integrating into real-world applications smart city systems can enhance practical impact. provides informative guide for researchers novices, providing understanding cutting-edge methods, systematic paths. It aims promote robust efficient contribute global management protection efforts.

Language: Английский

---

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 916, P. 170209 - 170209

Published: Jan. 24, 2024

Language: Английский

---

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China

Environmental Pollution, Journal Year: 2022, Volume and Issue: 315, P. 120392 - 120392

Published: Oct. 14, 2022

Language: Английский

---

Reveal the main factors and adsorption behavior influencing the adsorption of pollutants on natural mineral adsorbents: Based on machine learning modeling and DFT calculation

Separation and Purification Technology, Journal Year: 2023, Volume and Issue: 331, P. 125706 - 125706

Published: Nov. 15, 2023

Language: Английский

---