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Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies

Author

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  • Ana Belen Vicente

    (Department of Agricultural and Environmental Sciences, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain)

  • Pablo Juan

    (Department of Mathematics, Statistics Area, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
    IMAC, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain)

  • Sergi Meseguer

    (Department of Agricultural and Environmental Sciences, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain)

  • Laura Serra

    (CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
    Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17004 Girona, Spain)

  • Sergio Trilles

    (Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain)

Abstract

A statistical modelling of PM10 concentration (2006–2015) is applied to understand the behaviour, to know the influence of the variables to exposure risk, to treat the missing data to evaluate air quality, and to estimate data for those sites where they are not available. The study area, Castellón region (Spain), is a strategic area in the framework of EU pollution control. A decrease of PM10 is observed for industrial and urban stations. In the case of rural stations, the levels remain constant throughout the study period. The contribution of anthropogenic sources has been estimated through the PM10 background of the study area. The behaviour of PM10 annual trend is tri-modal for industrial and urban stations and bi-modal in the case of rural stations. The EU Normative suggests that 90% of the data per year are necessary to control air quality. Thus, interpolation statistical methods are presented to fill missing data: Linear Interpolation, Exponential Interpolation, and Kalman Smoothing. This study also focuses on testing the goodness of these methods in order to find the ones that better approach the gaps. After analyzing graphically and using the RMSE the last method is confirmed to be the best option.

Suggested Citation

  • Ana Belen Vicente & Pablo Juan & Sergi Meseguer & Laura Serra & Sergio Trilles, 2019. "Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5857-:d:279029
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
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    Cited by:

    1. Monika Załuska & Katarzyna Gładyszewska-Fiedoruk, 2020. "Regression Model of PM2.5 Concentration in a Single-Family House," Sustainability, MDPI, vol. 12(15), pages 1-15, July.
    2. Sergio Trilles & Ana Belen Vicente & Pablo Juan & Francisco Ramos & Sergi Meseguer & Laura Serra, 2019. "Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler," Sustainability, MDPI, vol. 11(24), pages 1-14, December.

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    More about this item

    Keywords

    PM10; trend; interpolation methods; Kalman Smoothing;
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