An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5 DOI Creative Commons

Pei Jiang,

C. Y. Gao,

Junrui Zhao

et al.

Environment International, Journal Year: 2024, Volume and Issue: 190, P. 108793 - 108793

Published: June 3, 2024

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses dilemma urban residents who wish to engage in outdoor exercise adopt active commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed proposed assist users by recommending routes with the least exposure dynamically issuing early risk warnings based on topologized digital maps, application programming interface (API), eXtreme Gradient Boosting (XGBoost) model, two-step spatial interpolation. A test of UAHNS's functions applications carried out Wuhan city. The results showed that, compared trained random forest (RF), LightGBM, Adaboost models, etc., XGBoost model performed better, R

Language: Английский

Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities DOI
Dié Tang,

Tan Mi,

Xi Zheng

et al.

Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 491, P. 137893 - 137893

Published: March 10, 2025

Language: Английский

Citations

1
An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5 DOI Creative Commons

Pei Jiang,

C. Y. Gao,

Junrui Zhao

et al.

Environment International, Journal Year: 2024, Volume and Issue: 190, P. 108793 - 108793

Published: June 3, 2024

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses dilemma urban residents who wish to engage in outdoor exercise adopt active commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed proposed assist users by recommending routes with the least exposure dynamically issuing early risk warnings based on topologized digital maps, application programming interface (API), eXtreme Gradient Boosting (XGBoost) model, two-step spatial interpolation. A test of UAHNS's functions applications carried out Wuhan city. The results showed that, compared trained random forest (RF), LightGBM, Adaboost models, etc., XGBoost model performed better, R

Language: Английский

Citations

1