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A time dependent Bayesian nonparametric model for air quality analysis

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  • Gutiérrez, Luis
  • Mena, Ramsés H.
  • Ruggiero, Matteo

Abstract

Air quality monitoring is based on pollutants concentration levels, typically recorded in metropolitan areas. These exhibit spatial and temporal dependence as well as seasonality trends, and their analysis demands flexible and robust statistical models. Here we propose to model the measurements of particulate matter, composed by atmospheric carcinogenic agents, by means of a Bayesian nonparametric dynamic model which accommodates the dependence structures present in the data and allows for fast and efficient posterior computation. Lead by the need to infer the probability of threshold crossing at arbitrary time points, crucial in contingency decision making, we apply the model to the time-varying density estimation for a PM2.5 dataset collected in Santiago, Chile, and analyze various other quantities of interest derived from the estimate.

Suggested Citation

  • Gutiérrez, Luis & Mena, Ramsés H. & Ruggiero, Matteo, 2016. "A time dependent Bayesian nonparametric model for air quality analysis," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 161-175.
    • Handle: RePEc:eee:csdana:v:95:y:2016:i:c:p:161-175
    DOI: 10.1016/j.csda.2015.10.002
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    References listed on IDEAS

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    Cited by:

    1. Camerlenghi, Federico & Lijoi, Antonio & Prünster, Igor, 2017. "Bayesian prediction with multiple-samples information," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 18-28.
    2. Pereira, Luz Adriana & Gutiérrez, Luis & Taylor-Rodríguez, Daniel & Mena, Ramsés H., 2023. "Bayesian nonparametric hypothesis testing for longitudinal data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
    4. Huijie Zhang & Ke Ren & Yiming Lin & Dezhan Qu & Zhenxin Li, 2019. "AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data," Sustainability, MDPI, vol. 11(10), pages 1-28, May.

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