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Deep learning PM2.5 concentrations with bidirectional LSTM RNN

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Abstract

A better understanding of spatiotemporal distribution of PM2.5 (particulate matter with diameter less than 2.5 micrometer) concentrations in a continuous space-time domain is critical for risk assessment and epidemiologic studies. Existing spatiotemporal interpolation algorithms are usually based on strong assumptions by restricting the interpolation models to the ones with explicit and simple mathematical descriptions, thus neglecting plenty of hidden yet critical influencing factors. In this study, we developed a novel deep-learning-based spatiotemporal interpolation model, which includes the bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) as the main ingredient. Our model is able to take into account both spatial and temporal hidden influencing factors automatically. To the best of our knowledge, it is the first time of applying the bidirectional LSTM RNN in the spatiotemporal interpolation of air pollutants concentrations. We evaluated our novel method using a dataset that contains daily PM2.5 measurements in 2009 over the contiguous southeast region of the USA. Results demonstrate a good performance of our model. We also conducted simulations to explore the properties of spatiotemporal correlations. In particular, we found the temporal correlation is stronger than the spatial correlation.

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Authors and Affiliations

  1. Department of Computer Science, Georgia Southern University, Statesboro, GA, 30460, USA

    Weitian Tong, Lixin Li & Andrew Hamilton

  2. Department of Geology and Geography, Georgia Southern University, Statesboro, GA, 30460, USA

    Xiaolu Zhou

  3. Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX, 77030, USA

    Kai Zhang

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  1. Weitian Tong
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  2. Lixin Li
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  3. Xiaolu Zhou
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  4. Andrew Hamilton
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  5. Kai Zhang
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Correspondence to Weitian Tong.

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Tong, W., Li, L., Zhou, X. et al. Deep learning PM2.5 concentrations with bidirectional LSTM RNN. Air Qual Atmos Health 12, 411–423 (2019). https://doi.org/10.1007/s11869-018-0647-4

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  • DOI: https://doi.org/10.1007/s11869-018-0647-4

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