Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China.
Environ Monit Assess
; 196(5): 487, 2024 Apr 30.
Article
em En
| MEDLINE
| ID: mdl-38687422
ABSTRACT
Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Monitoramento Ambiental
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Poluentes Atmosféricos
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Poluição do Ar
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Material Particulado
País/Região como assunto:
Asia
Idioma:
En
Revista:
Environ Monit Assess
Assunto da revista:
SAUDE AMBIENTAL
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China