Improving the construction and prediction strategy of the Air Quality Health Index (AQHI) using machine learning: A case study in Guangzhou, China
Year: 2024
Type: article
Abstract: as major contributors to mortality. The QGC method controlled the additive and synergistic effects among these air pollutants. Compared to the Standard-AQHI, the new RF-Alasso-QGC-AQHI demonstrated a stronger correlation with health outcomes, with an interquartile (IQR) increase associated with a 1.80 % (1.44 %, 2.17 %) increase in total mortality, and the best goodness of fit. Additionally, the hybrid Auto Regressive Moving Average-Long Short Term Memory (ARIMA-LSTM) successfully forecast the new AQHI, achieving a coefficient of determination (R²) of 0.961. The work demonstrated that the improved AQHI construction and prediction strategy more efficiently communicate and provide early warnings of the health risks of multiple air pollutants.
Authors: Lei Zhang, Yuanyuan Chen, Hang Dong, Di Wu, Sili Chen, Xin Li, Boheng Liang, Qiaoyuan Yang
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
Award: G8933364774
OpenAlex ID: W4404169766