Current Situation and Prospect of Geospatial AI in Air Pollution Prediction DOI Creative Commons

Chengqian Wu,

Siyu Lu, Jiawei Tian

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1411 - 1411

Published: Nov. 24, 2024

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming prominent. This article briefly reviews utilization geographic artificial intelligence (AI) in pollution. Firstly, this paper conducts a literature metrology analysis on research geographical AI used That is, 607 documents are retrieved from Web Science (WOS) using appropriate keywords, and Citespace to summarize hotspots frontier countries field. Among them, China plays constructive role fields research. The data characteristics Earth science direction field were proposed. It then quickly expanded investigate In addition, based summarizing current status Artificial Neural Network (ANN), Recurrent (RNN), hybrid neural network models predicting (mainly PM2.5), also proposes areas for improvement. Finally, prospects future study aims development trends quality, as well methods, provide

Language: Английский

Environmental Pollutants as Emerging Concerns for Cardiac Diseases: A Review on Their Impacts on Cardiac Health DOI Creative Commons
Vinay Kumar,

S Hemavathy,

Lohith Kumar Dasarahally Huligowda

et al.

Biomedicines, Journal Year: 2025, Volume and Issue: 13(1), P. 241 - 241

Published: Jan. 20, 2025

Comorbidities related to cardiovascular disease (CVD) and environmental pollution have emerged as serious concerns. The exposome concept underscores the cumulative impact of factors, including climate change, air pollution, chemicals like PFAS, heavy metals, on health. Chronic exposure these pollutants contributes inflammation, oxidative stress, endothelial dysfunction, further exacerbating global burden CVDs. Specifically, carbon monoxide (CO), ozone, particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur (SO2), pesticides, micro- nanoplastics been implicated in morbidity mortality through various mechanisms. PM2.5 leads inflammation metabolic disruptions. Ozone CO induce stress vascular dysfunction. NO2 cardiac remodeling acute events, metals exacerbate cellular damage. Pesticides microplastics pose emerging risks linked tissue Monitoring risk assessment play a crucial role identifying vulnerable populations assessing pollutant impacts, considering factors age, gender, socioeconomic status, lifestyle disorders. This review explores disease, discussing risk-assessment methods, intervention strategies, challenges clinicians face addressing pollutant-induced diseases. It calls for stronger regulatory policies, public health interventions, green urban planning.

Language: Английский

Citations

1
Ozone exposure and increased risk of age-related macular degeneration: Evidence from nationwide cohort and toxicological studies DOI Creative Commons
Guanran Zhang, Yanlin Qu, Xiaoling Wan

et al.

The Innovation, Journal Year: 2025, Volume and Issue: unknown, P. 100808 - 100808

Published: Feb. 1, 2025

Language: Английский

Citations

0
Metabolic Disruptions and Non-Communicable Disease Risks Associated with Long-Term Particulate Matter Exposure in Northern Thailand: An NMR-Based Metabolomics Study DOI Creative Commons
Churdsak Jaikang, Giatgong Konguthaithip, Yutti Amornlertwatana

et al.

Biomedicines, Journal Year: 2025, Volume and Issue: 13(3), P. 742 - 742

Published: March 18, 2025

Background/Objectives: Particulate matter (PM) is a primary health hazard associated with metabolic pathway disruption. Population characteristics, topography, sources, and PM components contribute to impacts. Methods: In this study, NMR-based metabolomics was used evaluate the impacts of prolonged exposure PM. Blood samples (n = 197) were collected from healthy volunteers in low- (control; CG) high-exposure areas (exposure; EG) Northern Thailand. Non-targeted metabolite analysis performed using proton nuclear magnetic resonance spectroscopy (1H-NMR). Results: Compared CG, EG showed significantly increased levels dopamine, N6-methyladenosine, 3-hydroxyproline, 5-carboxylcytosine, cytidine (p < 0.05), while biopterin, adenosine, L-Histidine, epinephrine, norepinephrine higher CG 0.05). These disturbances suggest that chronic particulate impairs energy amino acid metabolism enhancing oxidative stress, potentially contributing onset non-communicable diseases (NCDs) such as cancer neurodegenerative conditions. Conclusions: This study highlighted connection between sub-chronic PM2.5 exposure, disturbances, an risk (NCDs), stressing critical need for effective reduction strategies

Language: Английский

Citations

0
Particulate Matter and Cardiac Arrhythmias: From Clinical Observation to Mechanistic Insights at cardiac ion channels DOI

Damrongsak Jinarat,

Krekwit Shinlapawittayatorn, Siriporn C. Chattipakorn

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126168 - 126168

Published: March 1, 2025

Language: Английский

Citations

0
Improved Prediction of Hourly PM2.5 Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization DOI Creative Commons
Zuhan Liu,

Hong Xian-ping

Toxics, Journal Year: 2025, Volume and Issue: 13(5), P. 327 - 327

Published: April 23, 2025

To address the performance degradation in existing PM2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis integrate ant colony optimization (ACO) algorithm model optimization. Combining meteorological collaborative pollutant data, a (namely stacking-ACO-LSTM model) with much shorter consuming time than that of only long short-term memory (LSTM) networks suitable concentration is established. It can effectively filter out variables higher weights, thereby reducing predictive power model. The hourly trained tested using real-time monitoring data Nanchang City from 2017 to 2019. results show established has high accuracy predicting concentration, compared same without considering space efficiency defective mean square error (MSE) decreases about 99.88%, coefficient determination (R2) increases 2.39%. This study provides new idea cities.

Language: Английский

Citations

0
Association between wildfire-related PM2.5 and epigenetic aging: a twin and family study in Australia DOI Creative Commons
Yao Wu, Rongbin Xu, Shanshan Li

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 481, P. 136486 - 136486

Published: Nov. 13, 2024

Wildfire-related PM

Language: Английский

Citations

1
Current Situation and Prospect of Geospatial AI in Air Pollution Prediction DOI Creative Commons

Chengqian Wu,

Siyu Lu, Jiawei Tian

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(12), P. 1411 - 1411

Published: Nov. 24, 2024

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming prominent. This article briefly reviews utilization geographic artificial intelligence (AI) in pollution. Firstly, this paper conducts a literature metrology analysis on research geographical AI used That is, 607 documents are retrieved from Web Science (WOS) using appropriate keywords, and Citespace to summarize hotspots frontier countries field. Among them, China plays constructive role fields research. The data characteristics Earth science direction field were proposed. It then quickly expanded investigate In addition, based summarizing current status Artificial Neural Network (ANN), Recurrent (RNN), hybrid neural network models predicting (mainly PM2.5), also proposes areas for improvement. Finally, prospects future study aims development trends quality, as well methods, provide

Language: Английский

Citations

1