Hourly forecasting on PM2.5 concentrations using a deep neural network with meteorology inputs

The PM 2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM 2.5 concentra...

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Veröffentlicht in:Environmental monitoring and assessment Jg. 195; H. 12; S. 1510
Hauptverfasser: Liang, Yanjie, Ma, Jun, Tang, Chuanyang, Ke, Nan, Wang, Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.12.2023
Springer Nature B.V
Schlagworte:
Algorithms
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
China
data collection
Deep learning
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental science
infrastructure
issues and policy
Machine learning
Meteorological data
Meteorology
Monitoring/Environmental Analysis
Neural networks
Particulate emissions
Particulate matter
particulates
prediction
Predictions
Scientific advice
Suspended particulate matter
time series analysis
Beijing China
China
Data analytics
Air quality forecasting
PM
Machine learning
Meteorology
ISSN:0167-6369, 1573-2959, 1573-2959
Online-Zugang:Volltext
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Zusammenfassung:The PM 2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM 2.5 concentration prediction. In this study, we proposed a PM 2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM 2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter’s spatiotemporal correlation by concatenating the dataset with time series. The predicted PM 2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM 2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers.
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ISSN:0167-6369
1573-2959
1573-2959
DOI:10.1007/s10661-023-12081-0