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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Vydáno v:	Environmental monitoring and assessment Ročník 195; číslo 12; s. 1510
Hlavní autoři:	Liang, Yanjie, Ma, Jun, Tang, Chuanyang, Ke, Nan, Wang, Dong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.12.2023 Springer Nature B.V
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0167-6369 1573-2959 1573-2959
DOI:	10.1007/s10661-023-12081-0